A Hybridized Vector Optimal Algorithm for Multi-Objective Optimal Designs of Electromagnetic Devices

Abstract

Multiple-objective designs exist in most real-world engineering problems in different disciplines. A multi-objective evolutionary algorithm will face a challenge to obtain a series of compromises of different objectives, called Pareto optimal solutions, and to distribute them uniformly. In this regard, it is essential to keep the balance of local and global search abilities of such algorithms. Quantum-behaved particle swarm optimization (QPSO) is a population-based swarm intelligence algorithm, and differential evolutionary (DE) is another simple population-based stochastic search one for global optimization with real-valued parameters. Although the two optimizers have been successfully employed to solve a wide range of design problems, they also suffer from premature convergence and insufficient diversity in the later searching stages. This is probably due to the insufficient dimensional searching strength, especially for problems with many decision parameters. In this paper, a new multi-objective non-dominated optimal methodology combining QPSO, DE, and tabu search algorithm (QPSO-DET) is proposed to guarantee the balance between the local and global searches. The performances of the proposed QPSO-DET are compared with those of other two widely recognized vector optimizers using different case studies.