A Parallel Version of the Self-Adaptive Low-High Evaluation Evolutionary-Algorithm for Electromagnetic Device Optimization

被引：15
作者：
Dilettoso, Emanuele [1 ]
Rizzo, Santi Agatino [1 ]
Salerno, Nunzio [1 ]
机构：
[1] Univ Catania, Dipartimento Ingn Elettr Elettron & Informat, I-95125 Catania, Italy
来源：
IEEE TRANSACTIONS ON MAGNETICS | 2014年 / 50卷 / 02期
关键词：
Evolutionary computation; finite element methods (FEMs); induction heating; optimization methods; parallel algorithms; PARETO-OPTIMAL SOLUTIONS; FEM-DBCI METHOD; MULTIOBJECTIVE OPTIMIZATION;
D O I：
10.1109/TMAG.2013.2284928
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The self-adaptive low-high evaluation evolutionary-algorithm (SALHE-EA) is used to solve multimodal optimization problems. SALHE-EA is able to find the multiple optima of a single objective function (OF) and to give an idea of the fitness landscape in the neighborhood of these optima. This aspect is of crucial importance when the single OF is obtained using the weighted sum of the OFs, each related to a different goal of the optimization problem. This paper presents an improved version of SALHE-EA. This new version has several new features and, mainly, the suitability for parallelization.
引用
收藏
页码：633 / 636
页数：4
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