Wind Driven Butterfly Optimization Algorithm with Hybrid Mechanism Avoiding Natural Enemies for Global Optimization and PID Controller Design

• Published in: Journal of bionics engineering Vol. 20; no. 6; pp. 2935 - 2972
• Main Authors: He, Yang, Zhou, Yongquan, Wei, Yuanfei, Luo, Qifang, Deng, Wu
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.11.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Biochemical Engineering; Bioinformatics; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Comparative studies; Control systems design; Controllers; Design optimization; Engineering; Exploitation; Exploration; Genetic algorithms; Global optimization; Heuristic methods; Natural enemies; Pollination; Proportional integral derivative; Research Article; Search algorithms; Benchmark functions; Global optimization; Metaheuristic; Wind Driven Optimization (WDO); Proportional integral derivative (PID); Butterfly Optimization Algorithm (BOA)
• ISSN: 1672-6529; 2543-2141
• DOI: 10.1007/s42235-023-00416-z

This paper presents a Butterfly Optimization Algorithm (BOA) with a wind-driven mechanism for avoiding natural enemies known as WDBOA. To further balance the basic BOA algorithm's exploration and exploitation capabilities, the butterfly actions were divided into downwind and upwind states. The algorithm of exploration ability was improved with the wind, while the algorithm of exploitation ability was improved against the wind. Also, a mechanism of avoiding natural enemies based on Lévy flight was introduced for the purpose of enhancing its global searching ability. Aiming at improving the explorative performance at the initial stages and later stages, the fragrance generation method was modified. To evaluate the effectiveness of the suggested algorithm, a comparative study was done with six classical metaheuristic algorithms and three BOA variant optimization techniques on 18 benchmark functions. Further, the performance of the suggested technique in addressing some complicated problems in various dimensions was evaluated using CEC 2017 and CEC 2020. Finally, the WDBOA algorithm is used proportional-integral-derivative (PID) controller parameter optimization. Experimental results demonstrate that the WDBOA based PID controller has better control performance in comparison with other PID controllers tuned by the Genetic Algorithm (GA), Flower Pollination Algorithm (FPA), Cuckoo Search (CS) and BOA.