Discrete quantum-behaved particle swarm optimization based on estimation of distribution for combinatorial optimization

Abstract

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm. A quantum-behaved particle swarm optimization (QPSO) is also proposed by combining the classical PSO philosophy and quantum mechanics. These algorithms have been very successful in solving the global continuous optimization, but their applications to combinatorial optimization have been rather limited. Estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a novel discrete QPSO based on EDA for the combinatorial optimization problem. The proposed algorithm combines global statistical information extracted by EDA with local information obtained by discrete QPSO to create promising solutions. To demonstrate the performance of the proposed algorithm, experiments are carried out on the unconstrained binary quadratic programming problem which numerous hard combinatorial optimization problems can be formulated as. The results show that the discrete QPSO based on EDA have superior performance to other algorithms.