An innovative hybrid algorithm for solving combined economic and emission dispatch problems

Abstract

As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces a new mutation approach and crossover rate (to prevent premature convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) are inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6-and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10-and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances.