Quasi opposite-based learning and double evolutionary QPSO with its application in optimization problems

Although quantum-behaved particle swarm optimization (QPSO) algorithm has the advantages of few pa-rameters and simple implementation, it suffers from these problems of low precision while calculating high-dimensional complex problems and being caught in local optimum during the later stage of iteration. To tackle these shortages effectively, a modified QPSO (QDQPSO) algorithm is presented. In order to improve the search efficiency and convergence speed of the algorithm, the idea of quasi opposite-based learning is used in the initialization stage. For enhancing the overall performance of the algorithm, the double evolutionary mechanism is applied to update the individual location during the iterative process. Furthermore, perturbation at global optimum position and bound constraint handling are considered to help the algorithm to escape from local optimum and maintain the diversity of population. According to the results obtained by QDQPSO and other nine optimization algorithms on 28 benchmark functions under different dimensions, it is found that QDQPSO performs better on the accuracy and stability of the optimal solution. Subsequently, Wilcoxon rank -sum test and Friedman test demonstrate significant advantages of the improved algorithm. Finally, QDQPSO algorithm displays superior performance in solving five practical optimization problems compared to several optimization methods.

Automated summary

In this paper, a modified quantum-behaved particle swarm optimization (QDQPSO) algorithm is proposed to solve the problems of low precision and getting trapped in local optima in high-dimensional complex problems. The algorithm utilizes quasi opposite-based learning in the initialization stage to improve search efficiency and convergence speed. The double evolutionary mechanism is applied to update individual locations during the iteration process for enhancing overall performance. Perturbation at global optimum position and bound constraint handling are considered to help escape local optima and maintain population diversity. Experimental results show that QDQPSO performs better in terms of accuracy and stability of optimal solutions compared to other optimization algorithms. Wilcoxon rank-sum test and Friedman test demonstrate significant advantages of the improved algorithm. QDQPSO also exhibits superior performance in solving five practical optimization problems compared to several optimization methods.