In this paper attention has been paid to the study of multiobjective linear fractional programming problem (MOLFPP) by using fuzzy set theoretic approach. In this approach, MOLFPP is transformed into multiobjective linear programming problem (MOLPP) by suitable transformation. In algorithm-I, MOLFPP is transformed into MOLPP by using fuzzy set theory and the pareto optimal solution of the trans...

This paper proposes a practical methodology for the solution of multi-objective system reliability optimization problems. The new method is based on the sequential combination of multi-objective evolutionary algorithms and data clustering on the prospective solutions to yield a smaller, more manageable sets of prospective solutions. Existing methods for multiple objective problems involve eithe...

Decomposition-based methods are often cited as the solution to multi-objective nonconvex optimization problems with an increased number of objectives. These methods employ a scalarizing function to reduce the multi-objective problem into a set of single objective problems, which upon solution yield a good approximation of the set of optimal solutions. This set is commonly referred to as Pareto ...

The Pareto stability is one of solution concepts in two-sided matching markets with ties. It is known that there always exists a Pareto stable matching in the many-to-many setting. In this paper, we consider the following generalization of the Pareto stable matching problem in the many-to-many setting. Each agent v of one side has a matroid defined on the set of edges incident to v, and the set...

This paper presents a method to predict the relative objective weighting scheme necessary to cause arbitrary members of a Pareto solution set to become optimal. First, a polynomial description of the Pareto set is constructed utilizing simulation and high performance computing. Then, using geometric relationships between the member of the Pareto set in question, the location of the utopia point...

A problem that sometimes occurs in multiobjective optimization is the existence of a large set of Pareto-optimal solutions. Hence the decision making based on selecting a unique preferred solution becomes difficult. Considering models with rational B-efficiency relieves some of the burden from the decision maker by shrinking the solution set. This paper focuses on solving multiobjective optimiz...

Proposal in this paper is a method of interpolating the pareto optimal solution in Multiobjective Genetic Algorithms. In this method, some vacancies are maked up by resuming computation of Multiobjective Genetic Algorithms. A beginning field is set up by variables of both sides of the vacancy. Then, computation of Multiobjective Genetic Algorithms is started from the beginning field again. Visu...

numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

In this paper, we propose near admissible multiobjective search algorithms to approximate, with performance guarantee, the set of Pareto optimal solution paths in a state space graph. Approximation of Pareto optimality relies on the use of an epsilon-dominance relation between vectors, significantly narrowing the set of nondominated solutions. We establish correctness of the proposed algorithms...

 Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach --Paretobased multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuou...