Obtaining the fullest possible representation of solutions to a multi-objective optimization problem has been a major concern in MultiObjective Genetic Algorithms (MOGAs). This is because a MOGA, due to its nature, usually produces several clusters of solutions that does not cover the whole range of Pareto frontier. This poster paper indroduces an overview of a new approach, one that aims at ob...

| This paper describes a genetic algorithm using a Pareto partitioning method for multiobjective owshop scheduling. The purpose of the proposed method is to generate a set of non-dominated solutions that is properly distributed in the neighborhood of the trade-o surface. Simulation results show that the GA using the Pareto partitioning method has good performances better than the traditional GA...

In this paper, we propose a Pareto-based tabu search algorithm for multi-objective FJSP with Earliness/Tardiness (E/T) penalty. In the hybrid algorithm, several neighboring structure based approaches were proposed to improve the convergence capability of the algorithm while keep population diversity of the last Pareto archive set. In addition, an external Pareto archive was developed to record ...

abstract: in this paper, optimization of boeing 747 wing has been accomplished for cruise condition (mach number=0.85, flight altitude=35000 ft), where an optimal wing shape is proposed. objective functions are minimization of wing weight and drag force that as well as confining design parameters, two functional constrains are applied. the first functional constrain is fuel tank volume in the a...

In contrast to classical optimization problems, in multiobjective optimization several objective functions are considered at the same time. For these problems, the solution is not a single optimum but a set of optimal compromises, the so-called Pareto set. In this work, we consider multiobjective optimization problems that additionally depend on an external parameter λ ∈ R, so-called parametric...

A novel approach of differential evolution (DE) by incorporating the Pareto-optimal set is presented for optimising train movement through tuning fuzzy membership functions in mass transit systems. The Pareto-optimal based multiobjective optimisation approach uniquely provides a set of optimal non-dominated solutions to the decision-maker. From the set, the decision-maker selects satisfactory s...

1 AbstractIn this paper, a new distributed genetic algorithm for multiobjective optimization problems is proposed. In this approach, the island model is used with a distributed genetic algorithm and an operation of sharing for Pareto-optimum solutions is performed with the total population. In multiobjective optimization problems, the Pareto-optimum solutions should be derived for designers. Be...

The formation of project portfolio is a multi-objective problem that has a high impact on public and private organizations, and has generally been addressed by evolutionary algorithms. They often seek an approximation of the Pareto front, and then the decision maker must choose an only solution from the set. This is not a difficult task when you have to select a solution from a small set evalua...

The assignment of multiobjective human resources is a very important phase of the decisionmaking process, especially with respect to research and development projects where performance strongly depends on human resources capabilities. Unfortunately, the input data or related parameters are frequently imprecise / fuzzy owing to incomplete or unobtainable information, which can be represented as ...

Bargaining solutions that satisfy weak Pareto optimality, symmetry, and independence of equivalent utility representations are called standard. The Nash (1950) solution is the unique independent standard solution and the KalaiSmorodinsky (1975) solution is the unique monotonic standard solution. Every standard solution satisfies midpoint domination on triangles, or MDT for short. I introduce a ...