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The classical particle swarm optimization (PSO) algorithm is inspired on biological behaviors such as the social behavior of bird flocking and fish schooling. In this context, many significant improvements related the updating formulas and new operators have been proposed to improve the performance of the PSO algorithm in the literature. On the other hand, recently, as an alternative to the classical PSO, a quantumbehaved particle swarm optimization (QPSO) algorithm was proposed. The contribution of this paper is linked with a modified QPSO based on beta probability distribution and mutation differential operator. The effectiveness of the proposed modified QPSO algorithm is demonstrated by solving three kinds of optimization problems including two benchmark functions and a circular antenna design problem.

A Differential Beta Quantum-behaved Particle Swarm Optimization for Circular Antenna Array Design