Affiliations: Computational Intelligence and Technologies Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia
Correspondence:
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Corresponding author: Yun-Huoy Choo, Computational Intelligence and Technologies Research Group (CIT), Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. E-mail: [email protected]
Abstract: Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. It is used in rough reducts calculation for identifying optimally significant attributes set. This paper proposes a hybrid ant swarm optimization algorithm by using immunity to discover better fitness value in optimizing rough reducts set. By integrating PSO with ACO, it will enhance the ability of PSO when updating its local search upon quality solution as the number of generations is increased. Unlike the conventional PSO/ACO algorithm, proposed Immune ant swarm algorithm aims to preserve global search convergence of PSO when reaching the optimum especially under the high dimension situation of optimization with small population size. By combining PSO with ACO algorithms and embedding immune approach, the approach is expected to be able to generate better optimal rough reducts, where PSO algorithm performs the global exploration which can effectively reach the optimal or near optimal solution to increase fitness value as compared to the past research in optimization of attribute reduction. This research is also to enhance the optimization ability by defining a suitable fitness function with immunity process to increase the competency in attribute reduction and has shown improvement of the classification accuracy with its generated reducts in solving NP-Hard problem. The proposed algorithm has shown promising experimental results in obtaining optimal reducts when tested on 12 common benchmark datasets. Result for rough reducts and fitness value performance has been discussed and briefly explored in order to identify the best solution. The experimental analysis on the initial results of IASORR has been proven to offer a better quality algorithm and to maintain PSO's performance, which are also encouraging in t-test analysis, for most of the tested datasets.
Keywords: Rough reducts, particle swarm optimization, ant colony optimization, immunity, ant swarm optimization
