Chaos-improved Multiobjective Optimization Algorithms for Solution of Economic Dispatch Problem
AbstractEconomical dispatch (ED) problem is defined to obtain an equilibrium point between power generator and cost of each generator. As a conventional definition, the ED problem is defined as the sum of cost from each generator under constraints. Even total cost is defined as the main objective of the problem, the loss at the transmission lines is included as that total power generation must be equal to the loss at the transmission lines and demand at the load. However loss as the transmission line is an important topic for efficiency. Therefore, in this paper transmission loss is defined as a new objective without changing the constraints. This multi-objective ED problem is solved by using multiobjective optimization algorithms. For this purpose three MOEA are applied to the problem and compared with each other. These algorithms are Multi-Objective Particle Swarm Optimization (MOPSO), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Nondominated Sorting Genetic Algorithm II (NSGA-II). The performance of these algorithms is aimed to improve with chaosbased random number generator. In total, 6 set of results from multiobjective optimization (both conventional and chaos improved) results are compared and discussed with each other.