Abstract:
In This paper, we present a hybrid approach combining two optimization techniques
for solving Economic Emission Load Dispatch Optimization Problem EELD. The EELD
problem is formulated as a nonlinear constrained multiobjective optimization problem with
both equality and inequality constraints. Our approach integrates the merits of both genetic
algorithm (GA) and local search (LS). The proposed approach employs the concept of coevolution
and repair algorithm for handling nonlinear constraints. Also, it maintains a finitesized
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 we implement local search (LS) technique 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 the literature. The comparison demonstrates the superiority of
the proposed approach and confirms its potential to solve the multiobjective EELD problem.