Hao Chen¹, Tong Xu¹, Yutian Huang², Dabo Xin^1,*, Changting Zhong^1,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.075792 - 29 January 2026

Abstract Optimization problems are prevalent in various fields of science and engineering, with several real-world applications characterized by high dimensionality and complex search landscapes. Starfish optimization algorithm (SFOA) is a recently optimizer inspired by swarm intelligence, which is effective for numerical optimization, but it may encounter premature and local convergence for complex optimization problems. To address these challenges, this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm (MCPSFOA). The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA, which integrates the exploratory mechanisms of SFOA with the diverse search capacity of… More >

Qianqian Zhang¹, Yingmei Li^1,*, Jianjun Zhan^2,*, Shan Chen¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1251-1273, 2024, DOI:10.32604/cmc.2024.053892 - 15 October 2024

Abstract This research focuses on improving the Harris’ Hawks Optimization algorithm (HHO) by tackling several of its shortcomings, including insufficient population diversity, an imbalance in exploration vs. exploitation, and a lack of thorough exploitation depth. To tackle these shortcomings, it proposes enhancements from three distinct perspectives: an initialization technique for populations grounded in opposition-based learning, a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration, and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators. The effectiveness of the Improved Harris Hawks Optimization algorithm (IHHO) is assessed by… More >

F. S. Lobato¹, V. Steffen Jr², A. J. Silva Neto³

CMES-Computer Modeling in Engineering & Sciences, Vol.69, No.1, pp. 1-18, 2010, DOI:10.3970/cmes.2010.069.001

Abstract Differential Evolution Algorithm (DE) has shown to be a powerful evolutionary algorithm for global optimization in a variety of real world problems. DE differs from other evolutionary algorithms in the mutation and recombination phases. Unlike some other meta-heuristic techniques such as genetic algorithms and evolutionary strategies, where perturbation occurs in accordance with a random quantity, DE uses weighted differences between solution vectors to perturb the population. Although the efficiency of DE algorithm has been proven in the literature, studies indicate that the efficiency of the DE methods is sensitive to its control parameters (perturbation rate More >