Global optimization of an optical chaotic system by Chaotic Multi Swarm Particle Swarm Optimization

The control and estimation of unknown parameters of chaotic systems are a daunting task till date from the perspective of nonlinear science. Inspired from ecological co-habitation, we propose a variant of Particle Swarm Optimization (PSO), known as Chaotic Multi Swarm Particle Swarm Optimization (CMS-PSO), by modifying the generic PSO with the help of the chaotic sequence for multi-dimension unknown parameter estimation and optimization by forming multiple cooperating swarms. This achieves load balancing by delegating the global optimizing task to concurrently operating swarms. We apply it successfully in estimating the unknown parameters of an autonomous chaotic laser system derived from Maxwell–Bloch equations. Numerical results and comparison demonstrate that for the given system parameters, CMS-PSO can identify the optimized parameters effectively evolving at each iteration to attain the pareto optimal solution with small population size and enhanced convergence speedup.

► The work emphasizes the deterministic yet stochastic nature of chaotic carrier by embedding the merits of it in a Swarm Intelligence optimization technique. The outcome is a modified improved version of Particle Swarm Optimization inspired from the ecological cohabitation of species through commensalism relationship. It is applied to the task of global optimization of the unknown parameters of a laser system derived from Maxwell-Bloch’s Equations. Detailed comparative analysis reveals that the proposed version is a decentralized option for effectively tackling the problem of premature convergence with enhanced convergence speed. The research findings are vital from the perspective of control theory and forecasting.