This paper reviews some state-of-the-art hybrid multiobjective evolutionary algorithms (MOEAs) dealing with multiobjective optimization problem (MOP). The mathematical formulation of a MOP and some basic definition for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs.

We present a method to capture decision maker’s preferences in multiobjective problems and we discuss its use as a base for a decision maker – multiobjective optimization model interface. We illustrate the idea on a small but illustrative numerical example of the airport gate assignment problem.

The purpose of this paper is to characterize the weak Pareto optimality for multiobjective pseudoconvex problem. In fact, it is a first order optimality characterization that generalize the Karush-Kuhn-Tucker condition. Moreover, this work is an extension of the single-objective case [6] to the multiobjective one with pseudoconvex continuous functions. Mathematics Subject Classification: 46N10,...

The Pareto Archived Evolution Strategy 1 (PAES) KC99c, KC99b, KC99a], a local search algorithm for multiobjective optimization tasks, is compared with a modern, proven population-based EA, the Strength Pareto Evolutionary Algorithm (SPEA) of Zitzler and Thiele ZT98, ZDT99, ZT99]. Comparison is carried out with respect to six test functions designed by Deb, each of which is designed to capture a...

Combining ideas from evolutionary algorithms, decomposition approaches and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: MoMad. It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MoMad evolves three populations: popula...

Smoothed analysis of multiobjective 0–1 linear optimization has drawn considerable attention recently. In this literature, the number of Pareto-optimal solutions (i.e., solutions with the property that no other solution is at least as good in all the coordinates and better in at least one) for multiobjective optimization problems is the central object of study. In this paper, we prove several l...

This paper presents an annual multiobjective crop-mix planning as a problem of concurrent maximization of net profit and maximization of crop production to determine an optimal cropping pattern. The optimal crop production in a particular planting season is a crucial decision making task from the perspectives of economic management and sustainable agriculture. A multiobjective optimal crop-mix ...

Entitled: Metaheuristics for multiobjective capacitated location allocation on logistics networks and submitted in partial fulfillment of the requirements for the degree of complies with the regulations of the University and meets the accepted standards with respect to originality and quality. ABSTRACT Metaheuristics for multiobjective capacitated location allocation on logistics networks Yongl...

A large number of techniques, such a neural networks and neurofuzzy systems, are used to produce empirical models based in part or in whole on observed data. A key stage in the modelling process is the selection of features. Irrelevant or noisy features increase the complexity of the modelling problem, may introduce additional costs in gathering unneeded data, and frequently degrade modelling p...

Report The aim of this work is to make some investigations concerning duality for mul-tiobjective optimization problems. In order to do this we study first the duality for scalar optimization problems by using the conjugacy approach. This allows us to attach three different dual problems to a primal one. We examine the relations between the optimal objective values of the duals and verify, unde...