A modified quantum-based particle swarm optimization for engineering inverse problem

Abstract

Purpose The purpose of this paper is to explore the potential of standard quantum-based particle swarm optimization (QPSO) methods for solving electromagnetic inverse problems. Design/methodology/approach A modified QPSO algorithm is designed. Findings The modified QPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic inverse problems. More specially, the experimental results as reported on different case studies demonstrate that the proposed method can find better final optimal solution at an early stage of the iterating process (uses less iterations) as compared to other tested optimal algorithms. Originality/value The modifications include the design of a new position updating formula, the introduction of a new mutation strategy and a dynamic control parameter to intensify the convergence speed of the algorithm.

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

“…Therefore, many researchers have proposed different methodologies for β parameter to control the convergence behavior of the optimizer as stated in [18], [19].…”

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

“…To demonstrate the performance of the proposed MQPSO method, some standard benchmark functions f 1 $f 6 (Maolong et al, 2008;Rehman et al, 2017aRehman et al, , 2017b) and f 7 $ f 9 (Suganthan et al, 2005) are used. These test functions have been considered by different researchers to evaluate their proposed algorithms.…”

“…To validate the proposed method for engineering electromagnetic problems, a benchmark problem has been solved. In this case, the well-known benchmark TEAM workshop Problem 22 has been solved (An et al, 2011;Coco et al, 2012;Yang et al, 2013;Rehman et al, 2017aRehman et al, , 2017b. The TEAM workshop problem is the optimization of superconducting magnetic energy storage system (SMES) device.…”

“…Hu et al (2016) have presented a novel multi-objective nondominated optimal methodology by combining QPSO, DE and Tabu search algorithm to guarantee the balance between the exploration and exploitation searches, and Wang (2016) has presented an improved QPSO by using chaotic search method, adjustment of inertia weight and introduction of a neighborhood mutation mechanism. Also, the PSO and QPSO have been successfully applied to many electromagnetic inverse problems (Ho et al, 2013;Khan et al, 2017;Rehman et al, 2017aRehman et al, , 2017b.…”

An efficient quantum multiverse optimization algorithm for solving optimization problems

“…Therefore, many types of research have studied the theoretical and practical studies to merge quantum computing and evolutionary computation [9]. Some of these are quantum genetic algorithms [10], quantum inspired scatter search [9,11], quantum differential algorithm [12], mining large databases [13], 0-1 optimization problem [14], knapsack problem [15], traveling salesman problem [16], engineering inverse problem [17] and other areas of applications as in Gottfried and Yan [18]. Currently, quantum inspired algorithms are used to solve many combinatorial optimization problems as proposed [19].…”