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In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

The Pelican Optimization Algorithm (POA) is a meta-heuristic algorithm that emulates the behavior and hunting strategy of pelicans. This paper presents three modifications that enhance the algorithmic traversal of POA by incorporating ten chaotic mapping factors. These modifications aim to improve the efficiency and accuracy of the traversal process. Firstly, the pelican optimization algorithm with chaotic mapping factors added to population generation but random prey generation (COPOA); secondly, the pelican optimization algorithm with chaotic mapping factors applied to prey generation but random population generation (OCPOA); finally, the pelican optimization algorithm with the same chaotic mapping factor, different sequences generated by different random initial values are mapped to the population and prey generation (CCPOA). The results of simulations on 12 benchmark test functions demonstrate that the algorithm (COPOA) adding the chaotic mapping factor to the population generation alone has higher generalizability, convergence speed and finding accuracy compared with the standard POA algorithm. Among these modifications, the Sine-COPOA method performed the best in ten chaotic mapping factor comparison tests. Furthermore, a comparison is made between the Sine-COPOA and typical intelligent optimization algorithms to assess its competence in optimization. The results show that Sine-COPOA performs better in most of the 12 benchmark test functions. Finally, the engineering design problems (pressure vessel problem and 10-bar truss design) are optimized. The experimental results demonstrate that Sine-COPOA effectively solves both function optimization and engineering optimization problems. [ABSTRACT FROM AUTHOR]

The grey wolf optimizer (GWO) is an efficient meta-heuristic algorithm inspired by hunting mechanism of grey wolf. Although it has successfully solved many engineering problems, it still suffers form premature convergence and poor precision in some cases. This paper presents a hybrid grey wolf optimization algorithm (HGWO) with enhanced exploration and exploitation. In order to boost the efficacy of GWO, a new α, β and δ wolf selected method based on random subgroup strategy is proposed and two new global search formulas are designed to enhance the exploration ability. Meanwhile, the concept of greedy wolf is introduced into HGWO to enhance the exploitation ability. Greedy wolf has no hierarchy concept, and only hunting around the most valuable prey to obtain the maximum profit. A nonlinear factor is designed to control the number of greedy wolves to balance the exploration and exploitation. In order to investigate the effectiveness of the proposed HGWO, it was compared with GWO, CGWO (a varint of GWO) and some recently developed algorithms on CEC2017 benchmark functions and four engineering problems. Statistical tests were also employed to investigate the significance of the results. Experimental results and statistical tests demonstrate that the performance of the proposed HGWO is significantly better than that of GWO and other comparison algorithms. [ABSTRACT FROM AUTHOR]