Multiobjective economic/emission dispatch by multiobjective thetas-particle swarm optimisation

Abstract

In this study, a novel modified adaptive θ-particle swarm optimisation (MA θ-PSO) algorithm is presented to investigate the multiobjective economic/emission dispatch (MEED). θ-PSO algorithm is based on the phase angle vector and can generate a high-quality solution within the shorter calculation time in comparison with the original PSO and other evolutionary methods. However, θ-PSO algorithm is easy to fall into stagnation when the position of a particle is not improved for several generations. In order to avoid this shortcoming, the authors have proposed a modified θ-PSO algorithm by using a new mutation. Moreover, the inertia weight factor as a significant adjusting parameter of θ-PSO algorithm is tuned by using fuzzy IF/THEN rules such that the cognitive and the social parameters are self-adaptively adjusted. The proposed MA θ-PSO algorithm maintains a finite-sized repository of non-dominated solutions. As the cost and emission functions have conflicting behaviours, a fuzzy clustering technique is used to control the size of the repository. The proposed algorithm is tested on two standard IEEE test systems. The obtained results demonstrate the satisfying capability of the proposed method to generate well-distributed Pareto optimal non-dominated solutions of the MEED problem.