Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization
Expert Systems with Applications: An International Journal
Co-evolutionary particle swarm optimization to solve constrained optimization problems
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Parameter identification of chaotic dynamic systems through an improved particle swarm optimization
Expert Systems with Applications: An International Journal
Integrated Learning Particle Swarm Optimizer for global optimization
Applied Soft Computing
Chaotic sequences to improve the performance of evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.