Multiobjective Coyote Algorithm Applied to Electromagnetic Optimization

被引：7
|
作者：
Pierezain, Juliano [1 ]
Coelho, Leandro dos Santos [1 ,2 ]
Mariani, Viviana Cocco [1 ,3 ]
Lebensztajn, Luiz [4 ]
机构：
[1] Univ Fed Parana, Dept Elect Engn, Curitiba, PR, Brazil
[2] Pontificia Univ Catolica Parana, Ind & Syst Engn Grad Program PPGEPS, Curitiba, PR, Brazil
[3] Pontificia Univ Catolica Parana, Mech Engn Grad Program PPGEM, Curitiba, PR, Brazil
[4] Univ Sao Paulo, Lab Eletromagnetismo Aplicado, Escola Politecn, Sao Paulo, SP, Brazil
来源：
2019 22ND INTERNATIONAL CONFERENCE ON THE COMPUTATION OF ELECTROMAGNETIC FIELDS (COMPUMAG 2019) | 2019年
关键词：
Optimization methods; finite element method; multiobjective optimization; swarm intelligence; EVOLUTIONARY ALGORITHMS;
D O I：
10.1109/compumag45669.2019.9032768
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The Coyote Optimization Algorithm (COA) is a population-based nature-inspired metaheuristic for global optimization that considers the social relations of the coyote proposed originally to single-objective optimization. In this paper, the numerical results are reported to validate a novel proposed multiobjective COA (MOCOA) to solve the Testing Electromagnetic Analysis Method (TEAM) workshop benchmark problem 25. Simulation results demonstrate the validity of the proposed MOCOA to find nondominated solutions that represent good trade-offs among the objectives in the evaluated problem.
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页数：4
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