A New Cooperative PSO Approach with Landscape Estimation, Dimension Partition, and Velocity Control

A New Cooperative PSO Approach with Landscape Estimation, Dimension Partition, and Velocity Control

Wei-Po Lee (Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan), Ruei-Yang Wang (Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan) and Yu-Ting Hsiao (Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan)
DOI: 10.4018/joci.2012010102
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Abstract

Particle swarm optimization (PSO) has been proposed as an alternative to traditional evolutionary algorithms. Yet, more efficient strategies are still needed to control the trade-off between exploitation and exploration in the search process for solving complex tasks with high dimensional and multimodal objective functions. In this work, the authors propose a new PSO approach to overcome the search difficulties. Their approach first predicts the landscape type of a function for initial search settings, and then focuses on two search strategies for multimodal functions. One is a two-swarm cooperative strategy that controls search region and integrates partial and full dimension PSO search. The other strategy is to control the velocity of the particles in an adaptive way, according to how they move in the space. To evaluate the proposed approach, extensive experiments have been conducted and comparisons to several popular PSO variants have been made. Our experiments prove that the proposed approach can have better performance than others in most of the test cases.
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Similar to other EAs, PSO is also a population-based technique. The basic PSO algorithm contains a set of particles and operates in an iterative manner. Each particle is characterized by its position and velocity, which moves in the search space. The position of each particle represents the potential solution and is evaluated by a predefined evaluation (fitness) function. During the iterative search process, each particle remembers its previous best position and the best position of any particle in the swarm. Then, the particle uses the above position information to modify its position and velocity, and continues its movement in the search space.

To enhance the performance of individuals, the main operator in the PSO is velocity updating for the particles, which combines the best position obtained by the swarm of particles and the best position reached by a certain particle during its flying history. It has the effect that the particles move towards the best position of the swarm. In the original PSO, the velocity and the position of a particle at time step t+1 are updated from those at time step t by the following rules:

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