An improved quantum based particle swarm optimizer applied to electromagnetic optimization problems

Abstract

Purpose

The aim of this paper is to explore the potential of standard quantum particle swarm optimization algorithms to solve single objective electromagnetic optimization problems.

Design/methodology/approach

A modified quantum particle swarm optimization (MQPSO) algorithm is designed.

Findings

The MQPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic design problems. The numerical results as reported have demonstrated that the proposed approach can find better final optimal solution at an initial stage of the iterating process as compared to other tested stochastic methods. It also demonstrates that the proposed method can produce better outcomes by using almost the same computation cost (number of iterations). Thus, the merits or advantages of the proposed MQPSO method in terms of both solution quality (objective function values) and convergence speed (number of iterations) are validated.

Originality/value

The improvements include the design of a new position updating formula, the introduction of a new selection method (tournament selection strategy) and the proposal of an updating parameter rule.

Citation

Rehman, O.U., Yang, S. and Khan, S. (2018), "An improved quantum based particle swarm optimizer applied to electromagnetic optimization problems", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 37 No. 1, pp. 319-332. https://doi.org/10.1108/COMPEL-04-2017-0160