Abstract
Optimization metaheuristics is a powerful way to deal with many electromagnetic optimization problems. Their main advantages are that they don't require gradient computation, they are more likely to give a global optimum solution and have a higher degree of exploration and exploitation ability. Recently, the symbiotic organisms search (SOS) algorithm was proposed to solve single-objective optimization problems. SOS mimics the symbiotic relationship among the living beings. In order to extend the classical mono-objective SOS algorithm approach, this paper proposes a new multiobjective SOS (MOSOS) based on nondominance and crowding distance criterion. Furthermore, an improved MOSOS (IMOSOS) based on normal (Gaussian) probability distribution function also was proposed and evaluated. Results on a multiobjective constrained brushless direct current (dc) motor design show that the MOSOS and IMOSOS present promising performance.
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