Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO

Haijun Liu, Rui Zhou, Xiaoyong Zhong (), Yuan Yao, Weifeng Shan, Jing Yuan, Jian Xiao, Yan Ma, Kunpeng Zhang and Zhibin Wang
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Haijun Liu: School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
Rui Zhou: School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
Xiaoyong Zhong: School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
Yuan Yao: Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China
Weifeng Shan: Institute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, China
Jing Yuan: School of Information Engineering, Institute of Disaster Prevention, Langfang 065201, China
Jian Xiao: School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
Yan Ma: School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
Kunpeng Zhang: College of Computer Science and Technology, Jilin University, Changchun 130012, China
Zhibin Wang: Gientech Digital Technology Group Co., Ltd., Beijing 100192, China

Mathematics, 2024, vol. 12, issue 19, 1-41

Abstract: Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

Keywords: metaheuristic algorithms; Crested Porcupine Optimizer (CPO); composite Cauchy mutation strategy; adaptive dynamic adjustment strategy; population mutation strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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