Abstract
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 tobridge the gap between single solutions and Pareto optimal sets. The Pareto setincludes all rational choices, among which the decision maker has to select thefinal solution by trading the objectives against each other. The search is thennot 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 forPareto optimal solutions. The objective of this search is to achieve this balance,by introducing two practical methods that reduce the Pareto optimal set toachieve a smaller set called the “pruned pareto set”. Multiple, often conflicting objectives arise naturally in most real-worldoptimization scenarios. As Fast Bacterial Foraging algorithms possess severalcharacteristics that are desirable for this type of problem, this class of searchstrategies has been used for multi-objective optimization for more than adecade. Meanwhile Fast Bacterial Foraging algorithms multi-objectiveoptimization has become established as a separate sub discipline combining thefields of Fast Bacterial Foraging computation and classical multiple criteriadecision making. A new hybrid technique algorithm is presented for the solution of thecomprehensive model of real world problems. This method is developed insuch 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 regionand a local search Sequential Quadratic Programming (SQP) is used as a finetuning to determine the optimal solution at the end.