Abstract
Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.
Highlights
Optimization of high dimensional design problems with a multimodal objective function in electromagnetics has attracted more attention for exploiting stochastic approaches as deterministic methods are not capable of finding the global optimum solution to these problems
Premature convergence is still a key issue in the quantum particle swarm optimization (QPSO) algorithm, when it is used in complicated design problems
To elaborate the performance of our proposed algorithm, it is compared with some well-known optimization algorithms, including standard QPSO proposed by J
Summary
Optimization of high dimensional design problems with a multimodal objective function in electromagnetics has attracted more attention for exploiting stochastic approaches as deterministic methods are not capable of finding the global optimum solution to these problems. Premature convergence is still a key issue in the QPSO algorithm, when it is used in complicated design problems. In fundamental PSO, the basic equation comprises the classical mechanics terminology (velocity v(t) and position x(t)) of a particle in the search space to solve the optimization problem. From our earlier work we identified that the traditional QPSOs have a premature convergence problem due to the diversity loss at the final stages of the evolution process and the unbalancing between the global and local searches of the particle.
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