A New Improved Self Adaptive Particle Swarm Optimization Technique for Economic Load Dispatch

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Abstract This paper presents a new improved self adaptive particle swarm optimization technique to avoid premature convergence for economic load dispatch problem. Many evolutionary techniques such as particle swarm optimization (PSO), differential evolution (DE) have been applied to solve this problem and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. In this method, the inertia weight is made self adaptive depending on the population size and the fitness rank of the particle along with time variant acceleration coefficients. A thirteen-unit test system is considered to demonstrate the effectiveness of the proposed method. The results obtained by the proposed algorithm are compared with other classical as well as modern heuristic techniques. It is found that the proposed method can produced improved results.KeywordsParticle Swarm OptimizationDifferential EvolutionFuel CostInertia WeightPremature ConvergenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.