An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position

Keywords: PSO QPSO Mean best position Weight parameter WQPSO a b s t r a c t Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with weighted mean best position according to fitness values of the particles. It is shown that the improved QPSO has faster local convergence speed, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. The proposed improved QPSO, called weighted QPSO (WQPSO) algorithm, is tested on several benchmark functions and compared with QPSO and standard PSO. The experiment results show the superiority of WQPSO. Over the past several decades, population-based random optimization techniques, such as evolutionary algorithm and swarm intelligence optimization, have been widely employed to solve global optimization (GO) problems. Four well-known paradigms for evolutionary algorithms are genetic algorithms (GA) [1], evolutionary programming (EP) [2], evolution strategies (ES) [3] and genetic programming (GP) [4]. These methods are motivated by natural evolution. The particle swarm opti-misation (PSO) method is a member of a wider class of swarm intelligence methods used for solving GO problems. The method was originally proposed by Kennedy as a simulation of social behaviour of bird flock and was first introduced as an optimisation method in 1995 [5]. Instead of using evolutionary operators to manipulate the individuals as in other evolutionary algorithms, PSO relies on the exchange of information between individuals. Each particle in PSO flies in search space with a velocity, which is dynamically adjusted according to its own former information. Since 1995, many attempts have been made to improve the performance of the PSO [6,7]. As far as the PSO itself concerned, however, it is not a global optimization algorithm, as has been demonstrated by Van den Bergh [8]. In [9,10], Sun et al. introduce quantum theory into PSO and propose a quantum-behaved PSO (QPSO) algorithm, which can be guaranteed theoretically to find optimal solution in search space. The experiment results on some widely used benchmark functions show that the QPSO works better than standard PSO and should be a promising algorithm. In this paper, in order to balance the global and local searching abilities, we introduce a weight parameter in calculating the mean best position in QPSO to render the importance of particles …