A New Binary Particle Swarm Optimization Approach: Momentum and Dynamic Balance Between Exploration and Exploitation

Abstract

Particle swarm optimization (PSO) is a heuristic optimization algorithm generally applied to continuous domains. Binary PSO is a form of PSO applied to binary domains but uses the concepts of velocity and momentum from continuous PSO, which leads to its limited performance. In our previous work, we reformulated momentum as a stickiness property and velocity as a flipping probability to develop sticky binary PSO. The initial design provides a good base, but many key factors need to be investigated. In this article, we propose a new algorithm called dynamic sticky binary PSO by developing a dynamic parameter control strategy based on an investigation of exploration and exploitation in the binary search spaces. The proposed algorithm is compared with four state-of-the-art dynamic binary algorithms on two types of binary problems: 1) knapsack and 2) feature selection. The experimental results on the knapsack datasets show that the new velocity and momentum assist sticky binary PSO in evolving better solutions than the benchmark algorithms. On feature selection, the dynamic strategy takes the advantages of these two newly defined movement concepts to help the proposed algorithm to produce smaller feature subsets with higher classification performance. This is the first time in the binary PSO, the four important concepts, that is, velocity, momentum, exploration, and exploitation, are investigated systematically to capture the properties of the binary search spaces to evolve better solutions for binary problems.