Affiliations: [a] College of Electrical Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China | [b] Wind Power Technology Center of Gansu Electric Power Company, Gansu, China | [c] Gansu Wind Power Grid Connected Engineering Technology Research Center, Gansu, China
Correspondence:
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Corresponding author: Jiaqiang Yang, College of Electrical Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China. E-mail:yjq1998@163.com
Abstract: 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.
Keywords: Global optimization, inverse problem, mutation, particle swarm optimization
