A global particle swarm optimization algorithm applied to electromagnetic design problem

Khan, Shafiullah; Yang, Shiyou; Rehman, Obaid Ur

doi:10.3233/jae-160063

Cited by 15 publications

(6 citation statements)

References 44 publications

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“…1. Other researchers have already used these test functions for testing the performance of optimal algorithms [13,27,28]. All the problems have zero optimal solution.…”

Section: Resultsmentioning

confidence: 99%

“…For the verification of our suggested MPSO for solving the engineering problems, we will have to use it for solving an engineering design problem. The problem which we have chosen is the TEAM workshop problem 22 [27,28]. It is an optimization problem having 3 different parameters, about optimization of the configuration of a Superconducting Magnetic Energy Storage (SEMS) as shown in Fig.…”

Section: Applicationmentioning

confidence: 99%

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Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

et al. 2018

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Particle swarm optimization is a stochastic optimal search algorithm inspired by observing schools of fishes and flocks of birds. It is prevalent due to its easy implementation and fast convergence. However, PSO has been known to succumb to local optima when dealing with complex and higher dimensional optimization problems. To handle the problem of premutature convergence in PSO, this paper presents a novel adaptive inertia weight strategy and modifies the velocity update equation with the new Sbest term. To maintain the diversity of the population a particular radius r is introduced to impulse cluster particles. To validate the effectiveness of the proposed algorithm, various test functions and typical engineering applications are employed, and the experimental results show that with the changing of the proposed parameter the performance of PSO improves when dealing with these complex and high dimensional problems.

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“…1. Other researchers have already used these test functions for testing the performance of optimal algorithms [13,27,28]. All the problems have zero optimal solution.…”

Section: Resultsmentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

et al. 2018

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“…A standard electromagnetic design problem is the Team Workshop problem 22 of a superconducting magnetic energy storage (SMES) configuration with three parameters as stated in [23]- [25], is then solved using the proposed approach. As shown in Fig 8, the system contains two concentric coils.…”

Section: Electromagnetic Applicationmentioning

confidence: 99%

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Rehman

et al. 2020

IEEE Access

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Quantum inspired particle swarm optimization (QPSO) stimulated by perceptions from particle swarm optimization and quantum mechanics is a stochastic optimization method. Although, it has shown good performance in finding the optimal solution to many electromagnetic problems. However, sometimes it falls to local optima when dealing with hard optimization problems. Thus, to preserve a good balance between local and global searches to avoid premature convergence in quantum particle swarm optimization, this paper proposed three enhancements to the original QPSO method, the proposed method is called modified quantum particle swarm optimization (MQPSO) algorithm. Firstly, a novel selection technique is introduced that will choose the best particle among the population within the search domain to achieve a high-performance exploration. Secondly, a new mutation method is used to preserve the easiness of available QPSOs. Also, a dynamic parameter strategy is proposed for further facilitating the algorithm and tradeoff between exploration and exploitation searches. The experimental results obtained by solving standard benchmark functions and an electromagnetic design problem which is the superconducting magnetic energy storage (SMES) system available in both three parameters and eight parameters problems are reported to showcase the usefulness of the proposed approach.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Rehman

Yang

Khan

2018

COMPEL

Self Cite

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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.

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A global particle swarm optimization algorithm applied to electromagnetic design problem

Cited by 15 publications

References 44 publications

Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

Improving the Diversity of PSO for an Engineering Inverse Problem using Adaptive Inertia Weight

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

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

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