Economic emission load dispatch through fuzzy based bacterial foraging algorithm

Abstract

This paper presents a newly developed optimization approach involving a modified bacterial foraging algorithm (MBFA) applied for the solution of the economic and emission load dispatch (EELD) problem. The approach utilizes the natural selection of global optimum bacterium having successful foraging strategies in the fitness function. The bacterial foraging algorithm (BFA) appears to be a robust and reliable optimization algorithm for the solution of the EELD problems. To obtain the best compromising solution a fuzzy decision making approach using MBFA is applied to the standard IEEE 30-bus six generator test system and a Taiwan power system of 40 generating units with valve point loading effects. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, linear programming (LP), multi-objective stochastic search technique (MOSST), differential evolution (DE), non-dominated sorting genetic algorithm (NSGA), niched pareto genetic algorithm (NPGA), strength pareto evolutionary algorithm (SPEA) and fuzzy clustering based particle swarm optimization (FCPSO) performed in different central load dispatch centers to solve EELD problems. The quality and usefulness of the proposed algorithm is demonstrated through its application to two standard test systems in comparison with the other existing techniques. The current proposal was found to be better than, or at least comparable to them considering the quality of the solutions obtained.