A generalized evolutionary metaheuristic (GEM) algorithm for engineering optimization

• Published in: Cogent engineering Vol. 11; no. 1
• Main Author: Yang, Xin-She
• Format: Journal Article
• Language: English
• Published: Abingdon Cogent 31.12.2024; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Algorithm; Algorithms; Algorithms & Complexity; Artificial Intelligence; Computation; derivative-free algorithm; Design engineering; Design optimization; Dr Pham D T, Engineering, University of Birmingham, United Kingdom of Great Britain and Northern Ireland; Evolutionary algorithms; evolutionary computation; Evolutionary Computing; Heuristic methods; metaheuristic; nature-inspired computing; optimization; Optimization techniques; swarm intelligence
• ISSN: 2331-1916; 2331-1916
• DOI: 10.1080/23311916.2024.2364041

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed.