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

Year 2015, Volume: 12 Issue: 1, - , 01.05.2015

Roghiyeh Hosseinpourfard Mohammad Masoud Javidi

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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