A new elitist multi-objective stochastic search technique and its application to economic-emission dispatch problem in power systems

In this paper, a new multi-objective hybrid evolutionary algorithm (MOHEA) dubbed as elitist multi-objective stochastic search technique - II (EMOSST-II) which is capable of finding multiple Pareto-optimal solutions with good diversity in a single run is presented. It is applied for the solution of two-objective economic-emission dispatch problem (EED) in power systems. The features of EMOSST-II ensure better diversity and prevent premature convergence to ensure better non-dominated solutions and faster convergence. The computational performance of EMOSST-II for EED is investigated on the IEEE 30 bus 6 generator system, IEEE 57 bus 13 generator system. The results indicate that the performance of EMOSST-II is competitive when compared to the other state-of-the-art elitist Multi-objective Evolutionary Algorithms in terms of convergence to true Pareto-optimal front, maintenance of good spread in Pareto solutions, speed of convergence and scalability.