Author:
Song Pei-Cheng,Chu Shu-Chuan,Pan Jeng-Shyang,Yang Hongmei
Abstract
AbstractThis work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference54 articles.
1. Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734. https://doi.org/10.1007/s00500-018-3102-4
2. Bedford GO (1978) Biology and ecology of the phasmatodea. Annu Rev Entomol 23(1):125–149
3. BoussaïD I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117
4. Chai QW, Chu SC, Pan JS, Zheng WM (2020) Applying adaptive and self assessment fish migration optimization on localization of wireless sensor network on 3-D terrain. J Inf Hiding Multimed Signal Process 11(2):90–102
5. Chen CM, Chen L, Gan W, Qiu L, Ding W (2021) Discovering high utility-occupancy patterns from uncertain data. Inf Sci 546:1208–1229. https://doi.org/10.1016/j.ins.2020.10.001
Cited by
48 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献