Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm
This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field Optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Elsevier
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/73882/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959551287&doi=10.1016%2fj.swevo.2015.07.002&partnerID=40&md5=b766a2cabfaa1d0929f5bf5926c26f6f |
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| Summary: | This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field Optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, a possible solution is an electromagnetic particle made of electromagnets, and the number of electromagnets is determined by the number of variables of the optimization problem. EFO is a population-based algorithm in which the population is divided into three fields (positive, negative, and neutral); attraction-repulsion forces among electromagnets of these three fields lead particles toward global minima. The golden ratio determines the ratio between attraction and repulsion forces to help particles converge quickly and effectively. The experimental results on 30 high dimensional CEC 2014 benchmarks reflect the superiority of EFO in terms of accuracy and convergence speed over other state-of-the-art optimization algorithms. |
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