Weimin Zheng, Mingchao Si, Xiao Sui, Shuchuan Chu, Jengshyang Pan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2173-2196, 2023, DOI:10.32604/cmes.2023.019890

Abstract The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution. It has good performance in global optimization fields such as maximization. In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm. This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal. This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer… More >

Zonglin Fu¹, Pei Hu¹, Wei Li², Jeng-Shyang Pan^1,*, Shuchuan Chu¹

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 233-247, 2021, DOI:10.32604/iasc.2021.014192

Abstract The Equilibrium Optimizer (EO) algorithm is a novel meta-heuristic algorithm based on the strength of physics. To achieve better global search capability, a Parallel Equilibrium Optimizer algorithm, named PEO, is proposed in this paper. PEO is inspired by the idea of parallelism and adopts two different communication strategies between groups to improve EO. The first strategy is used to speed up the convergence rate and the second strategy promotes the algorithm to search for a better solution. These two kinds of communication strategies are used in the early and later iterations of PEO respectively. To check the optimization effect of… More >