Abstract
A novel hybrid multiobjective quantum genetic algorithm (HM-QGA) for economic emission load dispatch (EELD) optimization problem is presented. The EELD problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. HM-QGA are population based evolutionary algorithms that imitate quantum physics by introducing quantum bits for a basic probabilistic genotypic representation and hence better population diversity, and quantum gates for evolving the population of solutions. We use quantum genetic algorithm to exploits the power of quantum computing to speed up genetic algorithm procedure. We present methodology that allows the decision maker (DM) to be a partner in problem solving, where DM specifies input values (namely the weight values) according his needs. Simulation results on the standard IEEE 30-bus 6-generator test system show that the proposed algorithm outperforms other heuristic algorithms and is characterized by robustness, high success, fast convergence and excellent capability on global searching.