A generalized evolutionary metaheuristic (GEM) algorithm for engineering optimization

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...

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Vydáno v:Cogent engineering Ročník 11; číslo 1
Hlavní autor: Yang, Xin-She
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Cogent 31.12.2024
Taylor & Francis Ltd
Taylor & Francis Group
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2331-1916
2331-1916
DOI:10.1080/23311916.2024.2364041