The nondominated sorting genetic algorithm with elitism (NSGA-II) is widely used due to its good performance on solving multiobjective optimization problems. In each iteration of NSGA-II, truncation selection is performed based on the rank and crowding distance of each solution. There are, however, drawbacks in this process. These drawbacks to some extent cause overlapping solutions in the popu...

The multiple objective simplex algorithm and its variants work in the decision variable space to find set of all efficient extreme points linear programming (MOLP). Other approaches problem either entire solutions or a subset them also return corresponding values (nondominated points). This paper presents an extension multiobjective (MSA) generate nondominated no redundant ones. extended versio...

Abstrack In this paper, a new multiobjective evohrtionary algorithm for EnvironmentaUEconomic power Dispatch (EED) optimimtion problem is presented. The EED problem is formulated as a nonlinear constrainedmultiobjective optimization problem with both equatity and inequality constraints. A new Nondominated Sorting Genetic Atgorithm (NSGA) based approach is proposed to handle the problem as a tru...

| Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often connicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of diierent methods presented up to now remain mostly ...

We explore a portfolio constructive model, formulated in terms of satisfaction of a given set of technical requirements, with the minimum number of projects and minimum redundancy. An algorithm issued from robust portfolio modeling is adapted to a vector model, modifying the dominance condition as convenient, in order to find the set of nondominated portfolios, as solutions of a bicriteria inte...

In this paper we propose a modification of Benson’s algorithm for solving multiobjective linear programmes in objective space in order to approximate the true nondominated set. We first summarize Benson’s original algorithm and propose some small changes to improve computational performance. We then introduce our approximation version of the algorithm, which computes an inner and an outer appro...

Evolutionary algorithms (EA’s) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly...

Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled als...