Multiobjective Coyote Algorithm Applied to Electromagnetic Optimization

Abstract

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.