Reinforcement learning, specifically Q-learning, has gained a plethora of attention from researchers in recent decades due to its remarkable performance in various applications. This study proposes a novel Reinforcement Learning-inspired Tunicate Swarm Algorithm (RLTSA) that employs a Q-learning approach to enhance the convergence accuracy and local search efficacy of tunicates in TSA while preventing their local optimal entrapment. Firstly, a novel Chaotic Quasi Reflection Based Learning (CQRBL) strategy with ten chaotic maps is proposed to improve convergence reliability. Then, Q-learning is introduced and embedded with TSA by dynamically switching the learning mechanisms of CQRBL and ROBL strategies at different stages for distinct problems. These two strategies in the Q-learning approach significantly improve the efficiency of the proposed algorithm. The performance of RLTSA is evaluated on a set of 33 distinct functions, including the CEC'05 and CEC'19 test functions, as well as four engineering design problems, and its outcomes are statistically and graphically tested against the TSA and seven other eminent meta-heuristics. In addition, statistical tests, notably the Friedman, Wilcoxon rank-sum, and t-tests, have been employed to exemplify the dominance of the RLTSA. The experimental findings disclose that RLTSA outperforms the competing algorithms in the realm of real-world engineering design problems.

Nowadays, various metaheuristic algorithms, drawing inspiration from nature have emerged, demonstrating remarkable effectiveness in tackling complex issues in diverse fields. However, the existing algorithms have significant drawbacks in solving challenging applications, including poor convergence accuracy, a lack of exploration ability, and being prone to local optima. To alleviate these drawbacks, this study proposes a novel hybrid method, namely a hybrid grey wolf optimizer and salp swarm algorithm (HGWOSSA), which is a combination of GWO and SSA. The aim behind this hybridization is to merge and enhance the capabilities of exploitation and exploration in both GWO and SSA to generate both varied strengths. To evaluate the efficacy of the HGWOSSA, it is implemented on a set of 10 classical test functions, and its outcomes are statistically and graphically tested against the prominent GWO, SSA and PSO algorithms. In addition, statistical tests, including the Wilcoxon test and t-test, are employed to examine the significant variance among the proposed HGWOSSA algorithm over other algorithms. The experimental findings establish that the HGWOSSA approach reaches the global optimum values compared to GWO, SSA and PSO for solving optimization challenges. also, from statistical analysis, it is confirmed that among the winners of ten test functions, HGWOSSA ranks first in the competition.

The Artificial rabbit optimization (ARO) algorithm replicates the survival skills of rabbits in the wild. However, like other metaheuristic approaches it possesses significant drawbacks in solving challenging problems, including sluggish convergence rate, poor exploration ability and trapped in local optima region. To alleviate these shortcomings, a novel strategy, namely Modified Random Opposition (MRO) and ten chaotic maps are integrated with ARO, termed as MROCARO. This implementation MRO technique boost the population diversity and permits the population to escape from local optima while integration of chaotic map enhances the exploitation capability. To estimate the effectiveness of the MROCARO method, the well-known CEC2005, CEC2017, CEC2019 and CEC2008lsgo test functions are considered. Moreover, non-parametric tests that include the Wilcoxon rank-sum and Friedman rank test are performed to analyze the significant difference among the compared algorithms. Furthermore, the efficiency of the MROCARO algorithm has been evaluated on various structural problems and optimal sizing of renewable energy systems. The experimental findings demonstrate that MROCARO performed optimum solution with 100% renewable sources with the lowest levelized cost of electricity of 0.0934 $/kWh as compared to other methods. Also, the simulation findings reveal that MROCARO has immense potential for addressing global optimization and structural problems as contrasted to other competing algorithms.

The Grey Wolf Optimizer (GWO) algorithm is a very famous algorithm in the field of swarm intelligence for solving global optimization problems and real-life engineering design problems. The GWO algorithm is unique among swarm-based algorithms in that it depends on leadership hierarchy. In this paper, a Modified Grey Wolf Optimization Algorithm (MGWO) is proposed by modifying the position update equation of the original GWO algorithm. The leadership hierarchy is simulated using four different types of grey wolves: lambda (λ), mu (μ), nu (ν), and xi (ξ). The effectiveness of the proposed MGWO is tested using CEC 2005 benchmark functions, with sensitivity analysis and convergence analysis, and the statistical results are compared with six other meta-heuristic algorithms. According to the results and discussion, MGWO is a competitive algorithm for solving global optimization problems. In addition, the MGWO algorithm is applied to three real-life optimization design problems, such as tension/compression design, gear train design, and three-bar truss design. The proposed MGWO algorithm performed well compared to other algorithms.

The African Vultures Optimization Algorithm (AVOA), a newly developed swarm-intelligence meta-heuristics motivated by the scavenging and hunting behaviors of African vultures in the wild, has lately been extensively applied in many different domains. However, the AVOA still possesses significant drawbacks in solving challenging applications, including poor convergence accuracy, a lack of exploration ability, and being prone to local optima. To alleviate these drawbacks, a novel approach entitled “Boosted Dynamic Chaotic Opposite Learning (BDCOL)” technique is proposed and incorporated with AVOA, termed BDCOL-AVOA, to improve the performance of the AVOA. This study employs boosted dynamic chaotic opposite points to initialize the population and generation updating rather than opposite points. In BDCOL approach, an iterative-based logarithmic decreasing weight factor is introduced to regulate the complexity of the search domain, while the Chaotic Opposite Learning (COL) technique is implemented to systematically explore the search domain employing non-linear scaling behavior with the goal of a good trade-off between intensification and diversification of the algorithm. To evaluate the efficacy of the BDCOL-AVOA, it is implemented on a set of 23 classical CEC'05, 10 complex CEC'21, and 12 recently developed CEC'22 test functions, and its outcomes are statistically and graphically tested against the AVOA, along with several other meta-heuristics. In addition, statistical tests, notably the Friedman, Wilcoxon rank-sum, and t-tests, have been employed to exemplify the dominance of the BDCOL-AVOA. Furthermore, the BDCOL-AVOA is applied to several real-world engineering applications. The experimental findings have substantiated that BDCOL-AVOA has immense potential for addressing real-world engineering design problems.

The tunicate swarm algorithm (TSA) is a recently introduced bio-inspired optimization algorithm motivated by the foraging and swarming behaviour of bioluminescent tunicates. It has gained a lot of attention from the heuristic community because of its superior performance in solving various optimization problems. However, it is also easy to get stuck in the local optima, resulting in premature convergence when dealing with highly challenging optimization problems. To alleviate these shortcomings, this study presents an improved TSA termed random opposition based TSA (ROBTSA), which integrates a novel random opposition based learning (ROBL) technique into the conventional TSA. This proposed approach is implemented with jumping probability, which facilitates the algorithm to jump out of local optimal traps by enhancing the diversity of the tunicates. To test the efficacy of the proposed algorithm, experimentations are conducted on a set of thirteen standard test functions, comprising unimodal and multimodal functions. The proposed ROBTSA is tested against several well-known and advanced algorithms, including PSO, GWO, WOA, SCA, MVO, and STOA. In addition, it has been compared with the original TSA and its variant OBTSA. Further, it has been applied to solve two real-life engineering design problems: pressure vessel and tension/compression spring problems. The experimental outcomes exhibit that ROBTSA outperforms the other competing algorithms in terms of convergence rate, accuracy, and stability. Moreover, the performance of ROBTSA has been proven by statistical measures such as the Friedman test and Wilcoxon rank-sum test, demonstrating its potential in the realms of global optimization and real-life engineering design problems.

Q-learning, an eminent reinforcement learning (RL) approach, has garnered substantial research attention in recent years owing to its effectiveness in solving intricate problems and attain noteworthy results in a range of applications. In this study, the Mountain Gazelle Optimizer (MGO) is explored as a promising metaheuristic algorithm, primarily due to its biologically inspired mechanisms that emulate the adaptive and dynamic behaviors of gazelles in nature. However, despite its strong performance, MGO has inherent limitations, such as a tendency to become trapped in suboptimal search regions during early iterations, making it challenging to escape local optima. Therefore, to circumvent these shortcomings, this paper introduces a novel Q-learning-inspired Mountain Gazelle Optimizer (QLMGO), integrating chaotic and random opposite-based learning (ROBL) strategies to enhance optimization performance. The key innovation of QLMGO lies in its dynamic switching mechanism, enabled by Q-learning, which adaptively selects between ROBL and chaotic strategies to optimize the search process. Initially, Q-learning is utilized to regulate the switching mechanism, ensuring efficient exploitation of the search space. During the update phase, QLMGO dynamically chooses the most effective strategy, either ROBL for intensified local search or chaotic exploration for escaping local optima, to accelerate convergence towards the global optimal solution. The performance of QLMGO was rigorously evaluated against well-established optimization algorithms using 23 CEC2005 functions, 10 advanced CEC2019 functions, 30 CEC2017 test functions, and six real-world engineering problems. To ensure a robust and precise assessment, statistical analyses including the Wilcoxon rank-sum test, Friedman test, and t test were conducted. The empirical results from benchmark functions and engineering applications demonstrate the superiority of QLMGO in solving both constrained and unconstrained optimization problems efficiently, thereby validating its effectiveness as an innovative optimization approach.

The application of meta-heuristic algorithms has significantly increased in recent years to find optimal solutions for continuous optimization problems. The Golden Jackal Optimizer (GJO) is a recently proposed swarm-based algorithm that has been considered to be a promising model of a meta-heuristic. Despite its superior performance, the GJO algorithm has flaws, including getting stuck in the local optimal regions and a lack of exploration ability, resulting in premature convergence when dealing with highly challenging optimization problems. Therefore, to circumvent this drawback, this paper proposes a Q-learning strategy combined with a novel adaptive mix-weighted dynamic opposition-based strategy (AMD) and random opposition-based learning (ROBL) strategy named the AMDRO-GJO algorithm. At first, the Q-learning method establishes a switching mechanism between AMD and ROBL strategies for the algorithm’s exploration. Lastly, during the updating phase, AMDRO-GJO identifies the best scheme for the global best solution, enhancing the algorithm’s exploitation. The effort of the proposed AMDRO-GJO algorithm has been examined on 23 classical, CEC2017, and CEC2019 benchmark functions. In addition, non-parametric tests such as the Wilcoxon rank sum test and t-test have been carried out to check the significance difference of the algorithms. Furthermore, the efficiency of several real-world engineering challenges has been evaluated by comparing it with those of rival optimizers. These experimental outcomes reveal the proposed AMDRO-GJO algorithm’s outstanding performance in tackling multiple optimization problems.

By combining opposition-based learning techniques with conventional African Vulture Optimization (AVO), this study offers a notable improvement in the handling of optimization problems. Despite the limitations of AVO, such as issues involving extremely rough search spaces, more iterations or function evaluations are necessary. To overcome this limitation, our proposed paper, an enhanced opposition-based learning (EOBL), speeds up the convergence and, at the same time, assists the algorithm in escaping local optima. A combination of this new technique with AVO, the Enhanced Opposition-based African Vulture Optimizer (EOBAVO), is proposed. The performance of the suggested EOBAVO was evaluated through experiments using the CEC2005 and CEC2022 benchmark functions in addition to seven engineering challenges. Furthermore, statistical analyses, including the t-test and Wilcoxon rank-sum test, were conducted, and they demonstrated that the proposed EOBAVO surpasses several of the leading algorithms currently in use. The results indicate that the proposed approach can be regarded as a competent and efficient solution for complex optimization challenges.

The research is conducted based on the primary data available on the data portal which is gathered from different sources of the Government and the Private. There have been several efforts for analyzing and predicting future COVID-19 cases based on primary data. The present study is based on an inferential methodology which is one of the most widely used data science techniques in the study of events like COVID-19 time-series analysis. Analyzing and predicting the COVID-19 cases in upcoming months utilizing SIR, ARIMA models, and forecasting. The implementation of the proposed approach is demonstrated on real-time data of districts in Tamil Nadu. The current work serves to be of great importance in the prediction of the COVID-19 crisis in day-to-day life.

Renewable energy systems provide a dependable, environment-friendly, and cost-effective alternative for producing electricity in remote regions. The growing use of meta-heuristic algorithms is attributed to their ability to provide rapid, accurate, and optimal results for intricate optimization challenges. Therefore, in this work, an Evolved Opposition-based Mountain Gazelle Optimizer (EOBMGO) algorithm is explored to achieve the optimal design for the combination of off-gird hybrid renewable energy systems (HRES) that incorporate solar photovoltaic (PV) modules, wind turbines, and battery systems. The primary objective of the optimization process is to minimize the total net annual cost while maintaining an acceptable loss of power supply probability (LPSP), considering levelized energy costs and the generation of excess power. The designed EOBMGO technique has been evaluated for three distinct LPSP values (0%, 0.5%, and 1%), with each value tested across 25 independent runs and 50 iterations. The designed algorithm is then assessed against several established optimizers, including Grey Wolf Optimizer (GWO), Artificial Rabbit Optimization (ARO), Brown-bear Optimization Algorithm (BOA), and White Shark Optimizer (WSO). Statistical analysis has been performed to highlight the superiority of the EOBMGO algorithm over others, which included an evaluation of mean, standard deviation, variance, and crest values. The simulation outcomes revealed that the EOBMGO technique achieved a lower oscillation rate, a low standard deviation, and superior balancing of exploitation and exploration capabilities. Further, EOBMGO is found robust in the sensitivity analysis with variation in capital cost of the major components. These outcomes will provide researchers with a valuable reference for choosing the optimal technique for sizing problems.

Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been a continuous effort to develop new and efficient meta-heuristic algorithms. The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting strategy of Aquila birds in nature. However, in complex optimization problems, the AO has shown a sluggish convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, in this study, a new mechanism named Fast Random Opposition-Based Learning (FROBL) is combined with the AO algorithm to improve the optimization process. The proposed approach is called the FROBLAO algorithm. To validate the performance of the FROBLAO algorithm, the CEC 2005, CEC 2019, and CEC 2020 test functions, along with six real-life engineering optimization problems, are tested. Moreover, statistical analyses such as the Wilcoxon rank-sum test, the t-test, and the Friedman test are performed to analyze the significant difference between the proposed algorithm FROBLAO and other algorithms. The results demonstrate that FROBLAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.

Harnessing maximum wind energy's power output and efficiency is vital to combat environmental challenges tied to conventional fossil fuels. Wind power's cost-effectiveness and emission reduction potential underscore its significance. Efficient wind farm layout plays a pivotal role, both technically and commercially. Evolutionary algorithms show their potential while solving multi-objective wind farm layout optimization problems. However, due to the large-scale nature of the problems, existing algorithms are getting trapped into local optima and fail to explore the search space. To address this, the TAG-DMOEA algorithm is upgraded with an adaptive offspring strategy (AOG) for better exploration. The proposed algorithm is employed on a wind farm layout problem with real-time data of wind speed and direction from two different locations. Unlike mixed hub heights, fixed hub heights such as 60, 67, and 78 m are adopted to conduct the case studies at two potential locations with real-time statistical data for the investigation of improved results. The results obtained by TAG-DMOEA-AOG on six cases are compared with 10 state-of-the-art algorithms. Statistical tests such as Friedman test and Wilcoxon signed rank test along with post hoc analysis (Nemenyi test) confirmed the superiority of the TAG-DMOEA-AOG on all cases of the considered multi-objective wind farm layout optimization problem.

This study introduces a new human-based meta-heuristic algorithm, the Learning Cooking Algorithm (LCA), based on the opposition-based learning (OBL) strategy, namely the Opposition-based Learning Cooking Algorithm (OLCA). The proposed OLCA algorithm consists of four stages: the First stage, where the OBL strategy is implemented to improve the initial population; the second stage, where children learn from their respective mothers; the third stage, where children and mothers learn from chefs; and the fourth stage, where OBL is applied again to update the population. The proposed OLCA has been examined over 23 test functions, and the OLCA outcomes are equated with several popular and top-performing optimization algorithms. The statistical outcomes, such as the average (Ave), standard deviation (Std), Wilcoxon rank-sum test, and t-test, reveal that the outcomes of OLCA may effiectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the proposed OLCA has been employed to solve three real-world engineering problems, such as the tension/compression spring problem, the gear train problem, and the three-bar truss problem. The results demonstrate the OLCA's superiority and capability over other algorithms in solving engineering problems.

Over the last few decades, a number of prominent meta-heuristic algorithms have been put forth to address complex optimization problems. However, there is a critical need to enhance these existing meta-heuristics by employing a variety of evolutionary techniques to tackle the emerging challenges in engineering applications. As a result, this study attempts to boost the efficiency of the recently introduced bio-inspired algorithm, the Tunicate Swarm Algorithm (TSA), which is motivated by the foraging and swarming behaviour of bioluminescent tunicates residing in the deep sea. Like other algorithms, the TSA has certain limitations, including getting trapped in the local optimal values and a lack of exploration ability, resulting in premature convergence when dealing with highly challenging optimization problems. To overcome these shortcomings, a novel multi-strategy ameliorated TSA, termed the Quasi-Oppositional Chaotic TSA (QOCTSA), has been proposed as an enhanced variant of TSA. This enhanced method contributes the simultaneous incorporation of the Quasi-Oppositional Based Learning (QOBL) and Chaotic Local Search (CLS) mechanisms to effectively balance exploration and exploitation. The implementation of QOBL improves convergence accuracy and exploration rate, while the inclusion of a CLS strategy with ten chaotic maps improves exploitation by enhancing local search ability around the most prospective regions. Thus, the QOCTSA significantly enhances convergence accuracy while maintaining TSA diversification. The experimentations are conducted on a set of thirty-three diverse functions: CEC2005 and CEC2019 test functions, as well as several real-world engineering problems. The statistical and graphical outcomes indicate that QOCTSA is superior to TSA and exhibits a faster rate of convergence. Furthermore, the statistical tests, specifically the Wilcoxon rank-sum test and t-test, reveal that the QOCTSA method outperforms the other competing algorithms in the domain of real-world engineering design problems.

In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA’s superiority and capability over other algorithms in solving complex optimization problems.

The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting behavior of Aquila birds in nature. However, in complex optimization problems, the AO has shown a slow convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, a hybrid with AO and twelve chaotic maps has been proposed to adjust its main parameter. This new mechanism, namely the Chaotic Aquila Optimization (CAO) algorithm, is employed with chaotic maps with the AO algorithm. The proposed chaotic AO (CAO) approach takes seriously a variety of chaotic maps while setting the main AO parameter, which helps in managing exploration and exploitation. To validate the performance of the CAO algorithm, estimates for CEC 2005 and CEC 2022 test functions and the first chaotic map results are compared with the AO algorithm to select the best results of the CAO algorithm, and then CAO results are compared with nine popular optimization algorithms such as FFA, AVOA, MGO, AGTO, SSA, GWO, MVO, SCA, TSA, and AO. Moreover, statistical analyses such as the Wilcoxon rank-sum test and the t-test are performed to analyze the significant difference between the proposed CAO and other algorithms. Furthermore, the proposed CAO has been employed to solve six real-world engineering problems. The results demonstrate the CAO's superiority and capability over other algorithms in solving complex optimization problems. The results demonstrate that CAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.

The Mountain Gazelle Optimizer (MGO) algorithm has become one of the most prominent swarm-inspired meta-heuristic algorithms because of its outstanding rapid convergence and excellent accuracy. However, the MGO still faces premature convergence, making it challenging to leave the local optima if early-best solutions neglect the relevant search domain. Therefore, in this study, a newly developed Chaotic-based Mountain Gazelle Optimizer (CMGO) is proposed with numerous chaotic maps to overcome the above-mentioned flaws. Moreover, the ten distinct chaotic maps were simultaneously incorporated into MGO to determine the optimal values and enhance the exploitation of the most promising solutions. The performance of CMGO has been evaluated using CEC2005 and CEC2019 benchmark functions, along with four engineering problems. Statistical tests like the t-test and Wilcoxon rank-sum test provide further evidence that the proposed CMGO outperforms the existing eminent algorithms. Hence, the experimental outcomes demonstrate that the CMGO produces successful and auspicious results.

The grey wolf optimisation (GWO) algorithm is one of the popular meta-heuristic algorithms in evolutionary computation. However, the GWO algorithm has many drawbacks such as less accuracy, incapable of local searching competence, and low convergence speed. Therefore, in this paper an improvised grey wolf optimisation algorithm called IGWO is being introduced to compensate for these drawbacks of the GWO method by altering the surrounding behaviour along with the new position updating formula. Several well-known benchmark functions are considered to examine the accurateness and convergence of the modified version. The outcomes are analogised to the well-known algorithms like particle swarm optimisation algorithm, GWO algorithm, mean GWO algorithm, fast evolutionary programming and gravitational search algorithm. The experimental results showed that the newly modified IGWO can produce extremely superior results in terms of optimum objective functions and convergence speediness.

The most recent study trend is to combine two or more variations to improve the quality of solutions to practical and contemporary real-world global optimization challenges. In this work, a novel Sine Cosine Algorithm (SCA) and hybrid Grey Wolf Optimization (GWO) technique is tested on 10 benchmark tests. A hybrid GWOSCA is a mixture of the Sine Cosine Algorithm (SCA) for the exploration phase and the Grey Wolf Optimizer (GWO) for the exploitation phase in an undefined environment. The simulation findings reveal that the suggested hybrid technique outperforms, better than other known algorithms in the research community.

A recently developed swarm-based meta-heuristic algorithm namely Grey Wolf Optimization algorithm (GWO), which is based on the hunting and leadership behaviours of the grey wolves in nature, has shown superior performance when compared with existing meta-heuristic algorithms. However, like other approaches, the GWO has the limitation of poor exploitation ability and being stuck in local optima when solving challenging optimization problems. To overcome these limitations, a novel technique, namely “Enhanced Opposition-Based Learning” (EOBL), has been proposed and is implemented with the GWO algorithm. The EOBL technique is largely inspired by Opposition-Based Learning (OBL) and Random Opposition-Based Learning (ROBL) techniques to efficiently balance exploration and exploitation. As a result, the Enhanced Opposition-Based Grey Wolf Optimizer (EOBGWO), an innovative approach, is proposed to increase the effectiveness of the conventional GWO algorithm. To test the efficiency of the proposed EOBGWO method, it has been tested on the standard IEEECEC2005, IEEECEC2017, and IEEECEC2019 test functions, along with several real-life engineering design problems. Furthermore, to evaluate the effectiveness and stability of the proposed technique, it has been evaluated on the challenging IEEECEC2008 special session on large scale global optimization problems. The experimental outcomes and statistical measures such as the t-test and Wilcoxon rank-sum test demonstrate that the proposed EOBGWO method outperforms the other state-of-the-art algorithms in both global optimization and engineering design problems.

Nowadays, optimization techniques are required in various engineering domains in order to find optimal solutions for complex problems. As a result, there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space. The recently designed nature-inspired metaheuristic, named the Golden Jackal Optimization​ (GJO) algorithm, was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems. However, like other approaches, the GJO has the limitations of poor exploitation ability, ease to get stuck in a local optimal region, and an improper balancing of exploration and exploitation. To overcome these limitations, this paper proposes a novel contribution to GJO based on a new technique, namely the fast random opposition-based learning Golden Jackal Optimization algorithm (FROBL-GJO). The FROBL technique is mainly inspired by opposition-based learning (OBL) and random opposition-based learning (ROBL) techniques to enhance the optimization precision and convergence speed of the GJO algorithm. Furthermore, two other models, such as OBL-GJO and ROBL-GJO, are also proposed for comparison purposes. To examine the proficiency of the newly proposed FROBL-GJO algorithm, it has been examined with several well-known existing meta-heuristic algorithms while solving the CEC-2005 and CEC-2019 benchmark test functions and real-life engineering problems. The experimental outcomes and statistical tests reveal the superior performance of the proposed FROBL-GJO in solving both global optimization and engineering design problems. Hence, the findings of benchmark functions and engineering problems endorse that the proposed FROBL-GJO algorithm can be considered a promising method for solving complex optimization problems.

A novel bio-inspired meta-heuristic algorithm, namely the American zebra optimization algorithm (AZOA), which mimics the social behaviour of American zebras in the wild, is proposed in this study. American zebras are distinguished from other mammals by their distinct and fascinating social character and leadership exercise, which navies the baby zebras to leave the herd before maturity and join a separate herd with no family ties. This departure of the baby zebra encourages diversification by preventing intra-family mating. Moreover, the convergence is assured by the leadership exercise in American zebras, which directs the speed and direction of the group. This social lifestyle behaviour of American zebras is indigenous in nature and is the main inspiration for proposing the AZOA meta-heuristic algorithm. To examine the efficiency of the AZOA algorithm, the CEC-2005, CEC-2017, and CEC-2019 benchmark functions are considered, and compared with the several state-of-the-art meta-heuristic algorithms. The experimental outcomes and statistical analysis reveal that AZOA is capable of attaining the optimal solutions for maximum benchmark functions while maintaining a good balance between exploration and exploitation. Furthermore, numerous real-world engineering problems have been employed to demonstrate the robustness of AZOA. Finally, it is anticipated that the AZOA will accomplish domineeringly for forthcoming advanced CEC benchmark functions and other complex engineering problems.

A recently established swarm-based algorithm, namely, Mountain Gazelle Optimizer (MGO) which draws inspiration from social structure and hierarchy of wild mountain gazelles is competitive for solving optimization problems. However, the MGO has some drawbacks: when dealing with higher dimensions, early iterations could become stuck in suboptimal search area. It would be difficult for the MGO to abandon the local optimal solution if the early best solutions neglect the relevant search space. Therefore, to overcome these limitations, this paper offers an Evolved Opposition-based Learning (EOBL) mechanism which helps the algorithm to jump out of the local optima while accelerating the convergence speed. This novel mechanism is incorporating with MGO to propose Evolved Opposition-based Mountain Gazelle Optimizer (EOBMGO). The experiments are conducted with CEC2005 and CEC2019 benchmark functions, along with seven engineering challenges to examine the performance of the proposed EOBMGO. Furthermore, the statistical tests, like the t-test and Wilcoxon rank-sum test, are verified and demonstrate that the proposed EOBMGO outperforms the existing top-performing algorithms. The outcomes indicated that the proposed technique may be seen as an efficient and successful approach for complex optimization challenges.

Golden Jackal Optimization (GJO) is a recently developed nature-inspired algorithm that is motivated by the collaborative hunting behaviours of the golden jackals in nature. However, the GJO has the disadvantage of poor exploitation ability and is easy to get stuck in an optimal local region. To overcome these disadvantages, in this paper, an enhanced variant of the golden jackal optimization algorithm that incorporates the opposition-based learning (OBL) technique (OGJO) is proposed. The OBL technique is implemented into GJO with a probability rate, which can assist the algorithm in escaping from the local optima. To validate the efficiency of OGJO, several experiments have been performed. The experimental outcomes revealed that the proposed OGJO has more efficiency than GJO and other compared algorithms.

In the present investigation, the empirical correlation was successfully developed to predict the input and output responses of the multi-pass friction stir processing (FSP)/Al2O3 nanoparticles at a 95% confidence interval (C.I). The base metal AA6061 was characterized by nanoparticles Al2O3 within the structure of the coarse dendrite. These coarse and dendrites clusters were successfully broken by multi-pass FSP (MPFSP), refined the matrix grains and produced a homogenous microstructure in the stir zone (SZ). The developed model reveals that the nanoparticles Al2O3 and FSP passes were the dominating parameters to enhance the mechanical properties of the MPFSP/Al2O3. The ultimate tensile strength (UTS) and hardness were increased with increases in nanoparticles Al2O3 and the FSP passes. The optimized value of UTS, % strain and microhardness was observed as 220.07 MPa, 13.36%, and 98.44 HV, respectively, while the optimized value of nanoparticles Al2O3 and number of FSP passes were 9.65% and 1.72, respectively.

Optimisation problems containing huge total of decision variables are termed as large scale global optimisation problems which are often considered as abundant challenges to the area of optimisation. With presence of large number of decision variables, these problems also used to have the property of nonlinearity, discontinuity and multi-modality. Hence, the nature-inspired optimisation algorithms based on stochastic approaches are termed as great saviours than the deterministic approaches to handle these problems. However, the nature inspired optimisation algorithms also suffer from the jinx of dimensionality in the decision variable space. With increase of dimensions in the decision variable space, the complexity of the problem also increases exponentially. Hence, there is an immense need of proper guidance of choosing capable nature inspired algorithms to solve real-life large scale optimisation problems. In this paper, an attempt has been made to select the elite algorithm with proper justification. Hence, a number of works have been presented to analyse the results and to tackle the difficulty.

Whale optimization algorithm (WOA) is a novel and competitive swarm-based optimisation method that exceeds several previous metaheuristic algorithms in terms of simplicity and efficiency. Whale optimisation algorithm, a revolutionary nature-inspired algorithm, which mimics the behaviour patterns of humpback whales. WOA will interference with local optimization and greatly reduce accuracy for global optimization issue. To solve this type of problem, in this work, a new update equation has been developed named as modified whale optimisation algorithm (MWOA). Also, MWOA has been tested some CEC 2005 benchmark functions with dimension ranging from 2 to 30. The experimental outcomes show that the MWOA produce improved outcomes in terms of optimum value, convergence speed, and stability.

In this paper, a robust swarm-inspired algorithm has been proposed known as Cosine algorithm (CA) to solve the optimisation problem. The CA generates several initial random agents’ solution and requires all of them to change towards or outwards the ideal solution by means of mathematical model on Cosine function. A number of adaptive and random variables are also added into this method to promote exploitation and exploration of the search space at certain optimization milestones. The results of performance metrics and test functions demonstrate that the developed algorithm is capable of successfully exploring diverse areas of a search space, avoiding local optima, converging towards the worldwide optimum and exploiting potential parts of a search space through optimisation.

In this investigation, homogenously disseminated SiC reinforcement particles and a fine-grained structure was accomplished by multi-pass friction stir processing (MPFSP) of AA6082. The results revealed that refined grain structures with predominant high-angle grain boundaries were made in the stir zone in the 5th pass FSP due to severe plastic deformation and dynamic recrystallization. The MPFSP observed material flow around the cluster’s redistribution. At increased SiC concentration, the microstructure and electron backscatter diffraction (EBSD) examinations demonstrated that SiC reinforcement particles strongly inhibited grain boundary migration, resulting in an incessant decrease in grain size. The tensile properties and microstructure of the MPFSP/SiC were enhanced by employing a rotational tool speed (RTS) of 1450 rev/min, welding speed (WS) of 85 mm-min-1 with a tilt angle of 2°. The reinforcement particles were homogenously disseminated in the 5P FSP. The base metal AA6082's tensile strength was 219 ± 5 MPa with a % strain of 24.8 ± 0.3. After MPFSP/SiC on AA6082, the tensile strength was increased as the FSP pass increased. The higher tensile strength (298 ± 8 MPa) was observed at the 5P FSP, caused by fine grains during the dynamic recrystallization mechanism.

In this analysis, friction stir processing (FSP) was applied to the Si rich TIG welded joint to study the influence of multi-pass FSP (MPFSP) on microstructure, hardness and tensile properties. The TIG welding defects (coarse grain structure, porosity, microvoids, and solidification cracking) were eliminated, and the grain size of the TIG welded joint was decreased. As the FSP passes increases, the coarse eutectic Mg2Si and Al13Fe4 phases are converted into small phases. The coarse and elongated dendrite structure of the TIG welded joint was decreased after one FSP pass. The homogenization or modification of the primary α-Al exists in the TIG weldment was continuously improved as the TIG + FSP pass increased. The SZ of TIG + 3 pass FSP showed ultrafine grains of 3.42 µm compared to other welded specimens. The average ultimate tensile strength (UTS) of the TIG welded joint with filler ER4043 was observed to be 79.82 MPa, whereas the UTS of TIG + 1 pass FSP, TIG + 2 pass FSP, and TIG + 3 pass FSP was 97.87 MPa, 120.36 MPa, and 126.92 MPa respectively.

The original version of the article unfortunately contained an error. A data was inadvertently added in the second author’s name Sipokazi Mabuwa. The affiliation footnotes were also incorrect. The correct details are shown above. The original article has been corrected.

The welding process is used to join similar or dissimilar alloys, resulting in severe joint softening, uneven grain structure, and inevitable deficiencies. The friction stir process (FSP) can reduce the grain size and enhance the tensile properties. In this work, the FSP was applied to Si-rich TIG welded joints to enhance the tensile properties and microstructure of the TIG-welded joints by variation of rotational tool speed (TRS), and it was observed that the TIG welding defects (solidification defects, micro-voids, porosity, coarse grain structure) were removed, and the grain size of the TIG weldment was decreased. The coarse eutectic Al13Fe4 and Mg2Si phases were transformed into very small phases in the TIG + FSPed joints. The homogenization of the primary α-Al exists in the TIG welded joints was continuously enhanced as the TRS increased. The processed zone with high TRS (1100 rpm) demonstrated higher tensile strength (102.76 MPa), whereas the TIG weldment using filler ER4043 was employed to have an average tensile strength of 72.14 MPa. The ultrafine grain structure of 5.14 μm was found in the TIG + FSPed weldment with a TRS of 1100 rpm, while the coarse grain size of 20.85 μm was found in the TIG weldment.

In last few decades, the emission of greenhouse gasses has exponentially increased due to large production of electric power energy from conventional fossil fuels to pose critical environmental challenges. The renewable energies (REs) are establishing themselves as key technologies for reduction of carbon emissions, in addition to low cost and high efficiency. However, the operational limits and the power generation procedures of the renewable energies invite immense challenges. The uncertainty in production with precise and error free approximation make it very complicated. Hence, an effective approach with methodical organization of the renewable energies are the need of the hour for reliable and safe system. In this study, an IEEE 30-bus hybrid power system (HPS) problem consisting of conventional thermal generators and green energies like wind generators and solar photovoltaic are considered to become environmentally and economically capable than the existing ones. Several measures like penalty cost and reserve cost have been considered in this present study for addressing the uncertainty issues underestimation and overestimation respectively. Further, three hybrid configurations such as thermal-solar (TS), thermal-wind (TW) and thermal-wind-solar (TWS) are proposed to perform the cost effective analysis. The adopted hybrid power system is extremely complex and non-linear optimization problem. Hence, a recently proposed evolutionary algorithm namely competitive swarm optimization (CSO) algorithm is implemented to discover the optimum result for the variety goals like minimum production cost, carbon emission, voltage variation and loss of the power. The performance of CSO algorithm is compared with several state-of-the-art meta-heuristic algorithms such GA, PSO, CSA, ABC, and SHADE-SF. The extraordinary outcomes achieved in this work illustrate that the CSO method can successfully be applied to handle the complex, non-convex and non-linear hybrid power system problems.

In this paper, a novel competitive swarm optimizer (NCSO) is presented for large-scale global optimization (LSGO) problems. The algorithm is basically motivated by the particle swarm optimizer (PSO) and competitive swarm optimizer (CSO) algorithms. Unlike PSO, CSO neither recalls the personal best position nor global best position to update the elements. In CSO, a pairwise competition tool was presented, where the element that fails the competition are updated by learning from the winner and the winner particles are just delivered to the succeeding generation. The suggested algorithm informs the winner element by an added novel scheme to increase the solution superiority. The algorithm has been accomplished on high-dimensional CEC2008 benchmark problems and sampling-based image matting problem. The experimental outcomes have revealed improved performance for the projected NCSO than the CSO and several metaheuristic algorithms.

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

In this paper, the competitive swarm optimization (CSO) algorithm is applied for handling the economical load dispatch problem. The CSO algorithm is fundamentally encouraged by the particle swarm optimization (PSO) algorithm, but it does not memorize the personal best and global best to update the swarms. Rather in CSO algorithm, a pairwise competitive scenario was presented, where the loser particle is updated from the winner particle and the winner particles are directly accepted to the next population. The algorithm has been performed to find the generations of different units in a plant to reduce the entire fuel price and to maintain the total demand as well as the losses. The experimental study and investigations have revealed better performance for the CSO algorithm than the PSO and numerous state-of-art meta-heuristic algorithms in solving the economical power dispatch problem.

In this paper, an improvised competitive swarm optimizer (ICSO) is introduced for large-scale global optimization (LSGO) problems. The algorithm is fundamentally inspired by the competitive swarm optimizer (CSO) algorithm which neither remembers the personal best position nor global best position to update the particles. In CSO, a pair-wise competition mechanism was introduced, where the particle that loses the competition is updated by learning from the winner and the winner particles are simply passed to the next generation. The proposed algorithm introduces a new tri-competitive mechanism strategy to improve the solution quality. The algorithm has been performed on different dimensions of CEC2008 benchmark problems. The empirical results and analysis have shown better overall performance for the proposed ICSO than the CSO and many state-of-the-art meta-heuristic algorithms.

In this paper, a new Inherited Competitive Swarm Optimizer (ICSO) is proposed for solving large-scale global optimization (LSGO) problems. The algorithm is basically motivated by both the human learning principles and the mechanism of competitive swarm optimizer (CSO). In human learning principle, characters pass on from parents to the offspring due to the ‘process of inheritance’. This concept of inheritance is integrated with CSO for faster convergence where the particles in the swarm undergo through a tri-competitive mechanism based on their fitness differences. The particles are thus divided into three groups namely winner, superior loser, and inferior loser group. In each instances, the particles in the loser group are guided by the winner particles in a cascade manner. The performance of ICSO has been tested over CEC2008 benchmark problems. The statistical analysis of the empirical results confirms the superiority of ICSO over many state-of-the-art algorithms including the basic CSO.

In the recent literature a popular algorithm namely ‘Competitive Swarm Optimizer (CSO)’ has been proposed for solving unconstrained optimization problems that updates only half of the population in each iteration. A modified CSO (MCSO) is being proposed in this paper where two thirds of the population swarms are being updated by a tri-competitive criterion unlike CSO. A small change in CSO makes a huge difference in the solution quality. The basic idea behind the proposition is to maintain a higher rate of exploration to the search space with a faster rate of convergence. The proposed MCSO is applied to solve the standard CEC2008 and CEC2013 large scale unconstrained benchmark optimization problems. The empirical results and statistical analysis confirm the better overall performance of MCSO over many other state-of-the-art meta-heuristics, including CSO. In order to confirm the superiority further, a real life problem namely ‘sampling-based image matting problem’ is solved. Considering the winners of CEC 2008 and 2013, MCSO attains the second best position in the competition.

Waste stemmed from inappropriate management is a major challenge for perishable resources. Improvement of the inappropriate management has great potential to improve the efficiency of the resources. This research aims to maximize profit and reduce resource spoilage through a fitness value approach based on the decay rate of the perishable resources. A particular type of resource whose decay rate is uniform with time is considered here and is defined as uniform perishable resource. But here in this paper it is shown that the best way to utilize those resources is to follow the first method (i.e. to pick up the best resource first).

Multi-objective optimization involves optimizing a number of objectives simultaneously, and it becomes challenging when the objectives conflict each other, i.e., the optimal solution of one objective function is different from that of other. These problems give rise to a set of trade-off optimal solutions, popularly known as Pareto-optimal solution. Due to multiplicity in solutions, these problems were proposed to be solved suitably by using evolutionary algorithms which use a population approach in search procedure. So, these types of problems are called evolutionary multi-objective optimization (EMO) for handling multi-objective optimization problems. In this paper, an average-point-based EMO algorithm has been suggested for solving multi-objective optimization problem following NSGA-II mechanism (AP-NSGA-II) that emphasizes population members that are non-dominated. Finally, it has been shown how our two primary goals, convergence to Paretooptimal solution and maintenance of diversity among solutions, have been achieved.