![]() Shen, “ A novel swarm intelligence optimization approach: Sparrow search algorithm,” Syst. Jadon et al., “ Spider monkey optimization algorithm for numerical optimization,” Memetic Comput. Mirjalili et al., “ Grasshopper optimisation algorithm: Theory and application,” Adv. Grasshopper Optimization Algorithm (GOA), 24 24. Singh, “ Butterfly optimization algorithm: A novel approach for global optimization,” Soft Comput. Butterfly Optimization Algorithm (BOA), 23 23. Kumar, “ Emperor penguin optimizer: A bio-inspired algorithm for engineering problems,” Knowl. Stutzle, “ Ant colony optimization,” IEEE Comput. Basturk, “ On the performance of artificial bee colony (ABC) algorithm,” Appl. Mosavi, “ Chimp optimization algorithm,” Expert Syst. Chimp Optimization Algorithm (COA), 19 19. Mirjalili, “ Slime mould algorithm: A new method for stochastic optimization,” Future Gener. Chen, “ Harris hawks optimization: Algorithm and applications,” Future Gener. Kumar, “ Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications,” Adv. Mirjalili, “ Salp swarm algorithm: A bio-inspired optimizer for engineering design problems,” Adv. Askarzadeh, “ A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Comput. Mirjalili, “ Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Comput. Lewis, “ The whale optimization algorithm,” Adv. Whale Optimization Algorithm (WOA), 12 12. Mirjalili, “ Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowl. Alavi et al., “ Mixed variable structural optimization using Firefly algorithm,” Comput. Gandomi, “ Bat algorithm: A novel approach for global engineering optimization,” Eng. Eberhart, “ Particle swarm optimization,” in Proceedings of ICNN’95-International Conference on Neural Networks ( IEEE, Perth, WA, Australia, 1995), Vol. A few popular recently proposed swarm-based algorithms are the Particle Swarm Optimization (PSO), 7 7. ![]() Swarm-based algorithms: According to the characteristics of different organisms (such as insects and animals) in nature, these algorithms are inspired by collective intelligent behaviors (such as predatory behavior, aggressive behavior, and migration behavior) of different organisms. Schwefel, “ Evolution strategies-A comprehensive introduction,” Nat. Fogel, Artificial Intelligence through Simulated Evolution ( Wiley, 1998), pp. Song, “ An improved quantum-inspired differential evolution algorithm for deep belief network,” IEEE Trans. Improved Quantum-Inspired Differential Evolution Algorithm (MSIQDE), 4 4. Simon, “ Biogeography-based optimization,” IEEE Trans. Other popular evolutionary-based algorithms are the Biogeography-Based Optimization (BBO), 3 3. Price, “ Differential evolution-A simple and efficient heuristic for global optimization over continuous spaces,” J. The Differential Evolution Algorithm (DE) is another efficient evolutionary algorithm that mainly includes mutation, recombination, and selection steps. The Genetic Algorithm (GA) is a well-known evolutionary algorithm based on the laws of the survival of fittest from Darwin’s theory of evolution. Experimental and statistical results demonstrate that the proposed CMSSOA algorithm outperforms other variants of the SSOA algorithm and competitor approaches.Įvolutionary-based algorithms: These algorithms are usually inspired by the evolutionary process in nature, such as selection, variation, reorganization, and survival. Then, the performance of these variants was evaluated on 23 benchmark functions, and the various performances of the best variant were evaluated on a comprehensive set of 43 benchmark problems and three real-world problems compared to other optimizers. In addition, seven new variants of the SSOA algorithm are proposed employing the Gaussian mutation operator, Cauchy mutation operator, Lévy flights mutation operator, improved Tent chaos mutation operator, neighborhood centroid opposition-based learning mutation operator, elite opposition-based learning mutation operator, and simulated annealing algorithm combined with other mutation operators, namely, GSSOA, CSSOA, LFSSOA, ITSSOA, ESSOA, NSSOA, and CMSSOA, respectively. To overcome these shortcomings, initially, in this study, a shared SOA (SSOA) is proposed based on the combination of a sharing multi-leader strategy with a self-adaptive mutation operator. Seagull optimization algorithm (SOA) has the disadvantages of low convergence accuracy, weak population diversity, and tendency to fall into local optimum, especially for high dimensional and multimodal problems.
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