Shafiullah Khan

A Modified Particle Swarm Optimization Algorithm for Global Optimizations of Inverse Problems

Particle swarm optimization (PSO) is a population-based stochastic search algorithm inspired from the natural behavior of bird flocking or fish schooling for searching their foods. Due to its easiness in numerical implantations, PSO has been widely applied to solve a variety of inverse and optimization problems. However, a PSO is often trapped into local optima while dealing with complex and real world design problems. In this regard, a new modified PSO is proposed by introducing a mutation mechanism and using dynamic algorithm parameters. According to the proposed mutation mechanism, one particle is randomly selected from all personal best ones to generate some trial particles to preserve the diversity of the algorithm in the final searching stage of the evolution process. Moreover, the random variations in the two learning factors will help the particles to reach an optimal solution. In addition, the dynamic variation in the inertia weight will facilitate the algorithm to keep a good balance between exploration and exploitation searches. The numerical experiments on different case studies have shown that the proposed PSO obtains the best results among the tested algorithms.

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

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.

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

The purpose of this paper is to explore the potential of standard quantum-based particle swarm optimization (QPSO) methods for solving electromagnetic inverse problems.,A modified QPSO algorithm is designed.,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.,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 modified PSO algorithm with dynamic parameters for solving complex engineering design problem

ABSTRACT This paper proposed a new approach of particle swarm optimization (PSO). The proposed modified PSO algorithm is equipped with some specially designed mechanisms of adaptively updating algorithm parameters to preserve the diversity of the swarm and to keep the balance between exploration and exploitation searches. All these mechanisms help the algorithm to avoid the premature convergence and to strengthen its robustness. Experiments are conducted on different complicated, unimodal and multimodal test functions, as well as a typical engineering inverse problem, the TEAM Workshop problem 22. The numerical results illustrate that the proposed PSO shows better performance as compared to other well developed evolutionary algorithms.

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

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.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

An improved quantum particle swarm optimization applied to inverse problem in electromagnetics

The development of global optimal techniques for inverse problems in electromagnetics has been successful in the last few years. However, inspired from the classical Particle Swarm Optimization (PSO) algorithm and quantum mechanics, this work presents an improved Quantum based particle swarm optimization (QPSO) by using a tournament selection strategy. Also, a new index, called Tbest (tournament best), is incorporated into the QPSO to further enhance its performance. The feasibility and merit of the proposed approach are verified by mathematic functions and an electromagnetic inverse problem.

A particle swarm optimization method applied to global optimization of inverse problem

Particle Swarm Optimization (PSO) is a global optimal algorithm based on swarm intelligence. PSO is more popular due to its easiness in implementation and fast convergence speed. However, PSO will be trapped into local optima while it is used to solve complex, multimodal inverse problems. To establish a proper balance between the exploration and exploitation searches, this paper introduces a dynamic and adaptive mechanism for the three basic parameters. To preserve the diversity of the swarm, a particular best particle, which takes part in a mutation operation, is introduced in the modified PSO. The numerical results on a case study show that the proposed PSO finds the best results as compared to other ones.

A Modified Particle Swarm Optimization Algorithm for Engineering Optimizations

Particle Swarm Optimization (PSO) is a population based optimal method and very simple in both theory and numerical implementation. Nowadays, PSO has been recognized as a paradigm for numerical optimizations; however, PSO is easily trapped into a local optimum when solving multidimensional and complex problems. In order to overcome this difficulty, this paper presents a modified PSO with a dynamic inertia weight and an adaptive mutation operator. To verify the proposed PSO, we test it numerically on a set of well known bench mark functions as well as on an engineering problem, as to which it has shown better performance and efficiency while compared to the basic PSO and Beta PSO.