Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction

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Abstract

Multi-knowledge extraction is significant for many real-world applications. The nature inspired population-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multi-knowledge and to improve decision accuracy. In this Chapter, we introduce two nature inspired population-based computational optimization techniques namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for rough set reduction and multi-knowledge extraction. A Multi-Swarm Synergetic Optimization (MSSO) algorithm is presented for rough set reduction and multi-knowledge extraction. In the MSSO approach, different individuals encodes different reducts. The proposed approach discovers the best feature combinations in an efficient way to observe the change of positive region as the particles proceed throughout the search space. We also attempt to theoretically prove that the multi-swarm synergetic optimization algorithm converges with a probability of 1 towards the global optimal. The performance of the proposed approach is evaluated and compared with Standard Particle Swarm Optimization (SPSO) and Genetic Algorithms (GA). Empirical results illustrate that the approach can be applied for multiple reduct problems and multi-knowledge extraction very effectively.