Abstract
Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate.
Highlights
Particle swarm optimization (PSO) is a stochastic population-based algorithm proposed by Kennedy and Eberhart [1], which is motivated by the intelligent collective behavior of some animals such as flocks of birds or schools of fish
In order to balance the global and local searching abilities, we propose a set of weighted coefficients that can distinguish the fitness of particles to calculate the mean personal best position, and a novel way of computing the local attractor, a new kind of quantum-behaved particle swarm optimization with weighted mean personal best position and adaptive local attractor is designed for numerical optimization
The proposed ALA-quantum-behaved PSO (QPSO) algorithm was tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO
Summary
Particle swarm optimization (PSO) is a stochastic population-based algorithm proposed by Kennedy and Eberhart [1], which is motivated by the intelligent collective behavior of some animals such as flocks of birds or schools of fish. In PSO, each particle is regarded as a potential solution. PSO is trapped into the local optima, and premature convergence appears when it is applied to complex multimodal problems [4]. Kennedy and Eberhart [1], which is motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. The candidate solutions for PSO are called particles. The movements of the particles are guided by their own best known position called pbest and the entire swarm’s best known position called gbest. The position and velocity of the i −th particle is updated according to (1) and (2).
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