Abstract：The optimal design (inverse) problems in electrical engineering are generally characterized as highly nonlinear mathematical programming ones. If more than one objectives are involved,a set of non-dominated solutions,rather than a single one,are required for decision making. The search of multiple solutions increases further the complexity of the problem. Although extensively and successfully applied in different engineering disciplines,the non-dominance based evolutionary algorithms will encounter difficulties in solving optimal problems with more than three objectives,termed many-objective optimization problems. To solve many-objective optimal problems,the multiple single objective pareto sampling (MSOPS and MSOPS-II) algorithm is proposed with exclusive features of simplicities of implementations and low computational complexities. However,it is observed that this algorithm is not satisfactory in views of convergence and diversity performances. In this regard,an improved MSOPS is proposed. In the proposed algorithm,a crowding operation of target vectors is incorporated to preserve the diversity of the solutions; a non-uniform target vector updating mechanism and an external archive are introduced to effectively explore the search space to speed up the convergence rate. The numerical results on the synthesis of a linear array and a Yagi-Uda array demonstrate the feasibility and merits of the proposed algorithm.
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2013, Vol. 28 
