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Abstract

This paper presents a novel method for solving multi-objective optimization problems based on single-objective cellular genetic algorithm. In the proposed multi-objective cellular genetic algorithm, the objectives are divided into the primary objective and the secondary objective according to the preferences of a decision maker. The primary objective is used as the driving force for individual updating, while the secondary objective is employed as the bias force to select neighbors. The proposed approach has ensured that the secondary objective is also evolving in the optimal direction, as evidenced by the numerical results on both a mathematical test function and a prototype metamaterial unit as reported in this paper.

Keywords

Cellular genetic algorithm metamaterial negative permeability optimization

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Large permeability metamaterial design optimization for low frequency applications

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