科研之友
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摘要
Mutation operator is one of the mechanisms of evolutionary algorithms to guarantee the diversity in the search of an algorithm to help exploring undiscovered search spaces. Thus, in this work, a modified Quantum-inspired Particle Swarm Optimization (QPSO) algorithm for global optimizations of inverse problems is presented. In the proposed algorithm, a new mutation strategy is applied on the personal best particle to improve its global searching ability, also an improved Factor (iF) is incorporated into the position update equation of QPSO to further enhance its convergence speed. In addition, a new parameter updating strategy is proposed to tradeoff between the exploration and exploitation searches. To evaluate its performance, the proposed approach has been applied to a set of well-known mathematical test functions and an engineering inverse problem i.e. TEAM Workshop Problem 22. The experimental results demonstrate the effectiveness and advantage of the proposed method.
- 出版日期2017
- 单位浙江大学
