Abstract

In this paper, we present a hybrid approach combining two optimization techniques for solving economic emission load dispatch (EELD) optimization problem. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS), where it employs the concept of co-evolution and repair algorithm for handling nonlinear constraints, also, it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of -dominance. The use of -dominance also makes the algorithms practical by allowing a decision maker to control the resolution of the Pareto set approximation. To improve the solution quality, local search technique was implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test system. Simulation results with the proposed approach have been compared to those reported in literature. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem.

Key words: Economic emission load dispatch, evolutionary algorithms, multiobjective optimization, local search.