A Strategy-Selecting Hybrid Optimization Algorithm to Overcome the Problems of the No Free Lunch Theorem

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초록

When a conventional optimization algorithm is applied to diverse problems, the performance is not guaranteed because the optimization algorithm is tuned properly to a specific problem. To address this problem, a novel strategy-selecting hybrid optimization algorithm (SSHOA) is proposed. The proposed algorithm can autonomously and intelligently establish a strategy, which offers a better fitting algorithm according to a varying problem situation. The efficiency, accuracy, and reliability of the SSHOA are verified via mathematical test functions. To confirm electromagnetic performance capabilities, the proposed algorithm is applied to the optimization of an outer rotor permanent magnet machine.

키워드

Design optimizationfinite-element analysisgenetic algorithms (GAs)learning artificial intelligence (AI)MOTOR
제목
A Strategy-Selecting Hybrid Optimization Algorithm to Overcome the Problems of the No Free Lunch Theorem
저자
Kang, Jae-WooPark, Hyeon-JeongRo, Jong-SukJung, Hyun-Kyo
DOI
10.1109/TMAG.2017.2750204
발행일
2018-03
유형
Article
저널명
IEEE Transactions on Magnetics
54
3