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Abstract: Particle swarm optimization is a novel swarm-intelligence-based algorithm and a valid optimization technique. However, the algorithm suffers from the premature convergence problem when facing to complex optimization problem. In order to keep the balance between the global exploration and the local exploitation validly, the paper develops a knowledge-based cooperative particle swarm optimization (KCPSO). KCPSO mainly simulates the self-cognitive and self-learning process of evolutionary agents in special environment, and introduces a knowledge billboard to record varieties of search information. Moreover, KCPSO takes advantage of multi-swarm to maintain the swarm diversity and tries to guide their evolution by the shared information. Under the guide of the shared information, KCPSO manipulates each sub-swarm to go on with local exploitation in different local area, in which every particle follows a social learning behavior mode; at the same time, KCPSO carries out the global exploration through the escaping behavior and the cooperative behavior of the particles in different sub-swarms. KCPSO can maintain appropriate swarm diversity and alleviate the premature convergence validly. The proposed model was applied to some well-known benchmarks. The relative experimental results show KCPSO is a robust global optimization method for the complex multimodal functions. [Copyright &y& Elsevier]

This paper presents a grey particle swarm optimization learning algorithms to self classify different input patterns as correct subgroups. In our learning stratagem, a grey relational analysis (GRA) is combined with Particle swarm optimization (PSO) to develop a classification system. In our research, the GRA is more effective and accurate than the common-used Euclidean norm measure. The evolutional PSO is a very powerful learning algorithm to optimize the classifying task for complex, irregular and high dimensional input patterns. Several artificial data clustering examples compared with k-means method is presented to demonstrate the robustness of the proposed grey particle swarm optimization clustering algorithm. A real image classifying problem is also provided with the grey particle swarm optimization clustering algorithm. [ABSTRACT FROM AUTHOR]