Chaotic PSO using the Lorenz System: An Efficient Approach for Optimizing Nonlinear Problems

Abstract

Chaos particle swarm optimization (CPSO) is a novel optimization algorithm proposed in this paper. Evolutionary algorithms are one of the methods to solve optimization problems in various areas effectively. Particle swarm optimization (PSO) and genetic algorithms (GA) are the most popular evolutionary techniques. These algorithms adopt a random sequence for their parameters. However, these algorithms often lead to premature convergence, especially in complex nonlinear optimization problems. On the other hand, chaos theory studies the behavior of systems that are highly sensitive to their initial conditions and can hence generate a more variable range of numbers instead of random numbers. Therefore, this paper develops a new method that employs a Lorenz system, Tent map and Henon map to produce random numbers, when a random number is needed by the classical PSO algorithm. The experimental results show that the performance of CPSO is significantly better than the state-of-the-art techniques on PSO, GA and its combination with chaotic systems (CGA).

Keywords

• Chaos Particle Swarm Optimization,Optimization,Nonlinear Problem,Chaos Evolutionary Algorithm

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