Rong Li
Department of Computer, Xinzhou Teachers` University, Xinzhou, China
Hong-Bin Wang
Department of Computer, Xinzhou Teachers` University, Xinzhou, China
ABSTRACT
To better solve high-dimensional function optimization problems, for the defects of some optimization algorithms, such as premature convergence and low accuracy in Particle Swarm Optimization (PSO) and slow convergence in Bacterial Foraging Algorithm (BFA), this study presents a self-adaptive hybrid intelligent optimization algorithm based on BFA and PSO (ABSO for short). The ABSO algorithm first realizes dynamic no-linear self-adaptive improvement for learning factors and inertial weight of PSO and chemotaxis step length of BFA, respectively. After chemotaxis operation of BFA being finished, the optimization updating mechanism of PSO is introduced to continue to update bacterial location to help BFA escape from local optima which combines organically the optimization update mechanism of PSO and the chemotaxis update mechanism of BFA and well balances the global search and local development capabilities. Simulation results on four benchmark functions show that the ABSO algorithm is superior to BFA, PSO, self-adaptive PSO and other two kinds of BFA hybrid algorithm in convergence speed, accuracy and robustness. This proves the validity of the ABSO algorithm in high-dimensional function optimization problems.
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How to cite this article
Rong Li and Hong-Bin Wang, 2013. A Self-Adaptive Hybrid Intelligent Optimization Algorithm. Information Technology Journal, 12: 3058-3066.
DOI: 10.3923/itj.2013.3058.3066
URL: https://scialert.net/abstract/?doi=itj.2013.3058.3066
DOI: 10.3923/itj.2013.3058.3066
URL: https://scialert.net/abstract/?doi=itj.2013.3058.3066
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