多目标Coyote算法在电磁优化中的应用

2019 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG) Pub Date : 2019-07-01 DOI:10.1109/COMPUMAG45669.2019.9032768

Juliano Pierezan, L. dos Santos Coelho, V. Mariani, L. Lebensztajn

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引用次数: 7

摘要

COA算法是一种基于种群的自然启发的全局优化元启发式算法，它考虑了土狼的社会关系，最初提出了单目标优化。本文的数值结果验证了一种新的多目标COA (MOCOA)方法解决测试电磁分析法(TEAM)车间基准问题25的有效性。仿真结果证明了所提出的MOCOA在寻找非支配解方面的有效性，这些非支配解代表了评估问题中目标之间的良好权衡。

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Multiobjective Coyote Algorithm Applied to Electromagnetic Optimization

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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来源期刊

2019 22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG)

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