An Improved Particle Swarm Algorithm Based on Cultural Algorithm for Constrained Optimization
This paper develops an improved particle swarm optimization algorithm based on cultural algorithm for constrained optimization problems. Firstly, chaos method is utilized in the initialization process of single swarm in population space to assure the sear
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Abstract. This paper develops an improved particle swarm optimization algorithm based on cultural algorithm for constrained optimization problems. Firstly, chaos method is utilized in the initialization process of single swarm in population space to assure the searching breadth, and evolves with standard particle swarm optimization (PSO). Secondly, fixed proportion elites are selected from population space to construct the swarm of belief space through acceptance function. Then, the belief space updates its normative knowledge and situational knowledge according to the elite particles, and the elite-swarm in the belief space performs PSO operation according to the update knowledge and generates new particles. After that, the belief space renews the knowledge again, and passes down the new knowledge which has been updated twice to give better guidance to all the particles in the population space. The efficiency of the initialization strategy and the double evolving knowledge strategy are verified in six constrained optimization problems. Keywords: particle swarm optimization, cultural algorithm, constrained optimization.
1 Introduction In the last decade, evolutionary algorithm has gained more and more attention and been used in diverse application fields. In 1995, Kennedy and Eberhart [1] developed PSO which has many attractive characteristics, such as easy for understanding and implementation. However, a few deficiencies like trapping in local optimum and slow convergence which degrade the performance of PSO. In order to compensate these drawbacks, PSO is embedded into the framework of standard Cultural Algorithm (CA) which is proposed by Reynolds, R.G. [2]. Studies mainly focus on the improvements of parameters and the modifications on structure of population space [3][4]. In recent years, some researches begin to add some operations in belief space [5][6][7]. Inspired by their work, an improved particle swarm optimization based on cultural algorithm (IPSOCA) is proposed in this paper. Firstly, the chaos method is utilized in the initialization process to assure the searching breadth. Secondly, fixed proportion * Corresponding author. H. Tan (Ed.): Knowledge Discovery and Data Mining, AISC 135, pp. 453–460. © Springer-Verlag Berlin Heidelberg 2012 springerlink.com
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elites are selected from population space to construct the swarm of belief space. Then, the belief space updates its normative knowledge and situational knowledge, and the elite-swarm performs PSO operation according to the update knowledge and generates new particles. After that, the belief space renews the knowledge again and passes down the knowledge which has been updated twice to give better guidance to all the particles in the population space. The initialization strategy and the double evolving knowledge strategy are verified in six constrained optimization problems. The contents are organized as follows: Section 2 briefly introduces PSO and the PSO based on CA (PSOCA). Section 3 presents the methodology of IPSOCA we used.
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