Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, 300130, China

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Abstract—In this paper, a novel intelligent optimization algorithm - Artificial Tribe Algorithm (ATA) is presented based on the analyses of the principle and uniform framework of the Bionic Intelligent Optimization Algorithms (BIOA). ATA simulates the existent skills of the natural tribes, and actualizes the optimization purpose through the propagation and migration behaviors of the tribes. The main factors which influence the performance of ATA have been discussed. ATA is used for unconstrained and constrained functions optimization and the results produced by ATA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Fish-Swarm Algorithm (AFSA) have been compared. The results show that ATA is a powerful algorithm for global optimization problems.

Index Terms—artificial tribe algorithm, bionic intelligent optimization algorithm, optimization, swarm intelligence, genetic algorithm, particle swarm algorithm, artificial fishswarm algorithm

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Cite: Tanggong Chen, Youhua Wang and Jianwei Li, "Artificial Tribe Algorithm and Its Performance Analysis," Journal of Software vol. 7, no. 3, pp. 651-656, 2012.