Solution Of Multi - Objective Optimization Power System Problems Using Hybrid Algorithm

Generally the two most common approaches to solve multiple objectives are:combine them into a single objective function and obtain a single solution,obtain set of non-dominated Pareto optimal solutions. Thus there is a need to bridge the gap between single solutions and Pareto optimal sets. The Pareto set includes all rational choices, among which the decision maker has to select the final solution by trading the objectives against each other. The search is then not for one optimal solution but for a set of solution that are optimal in aborder sense. There are a number of techniques to search the solution for Pareto optimal solutions. The objective of this search is to achieve this balance,by introducing two practical methods that reduce the Pareto optimal set to achieve a smaller set called the “pruned pareto set”. Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As Fast Bacterial Foraging algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multi-objective optimization for more than a decade. Meanwhile Fast Bacterial Foraging algorithms multi-objective optimization has become established as a separate sub discipline combining the fields of Fast Bacterial Foraging computation and classical multiple criteria decision making. A new hybrid technique algorithm is presented for the solution of the comprehensive model of real world problems. This method is developed in such a way that a simple Fast Bacterial Foraging algorithms is applied as abase level search, which can give a good direction to the optimal global region and a local search Sequential Quadratic Programming (SQP) is used as a fine tuning to determine the optimal solution at the end.