An Elitist Genetic Algorithm for Multiobjective Optimization
نویسندگان
چکیده
Solving multiobjective engineering problems is a very difficult task due to, in general, in these class of problems, the objectives conflict across a high-dimensional problem space. In these problems, there is no single optimal solution, the interaction of multiple objectives gives rise to a set of efficient solutions, known as the Pareto-optimal solutions. During the past decade, Genetic Algorithms (GAs)[1] were extended in order to track this class of problems, as the Non-dominated Sorting Genetic Algorithm (NSGA) suggested by Srinivas and Deb [2]. These multiobjective approaches explore some features of GAs to tackle these kind of problems, in particular:
منابع مشابه
An Evolution Strategy for Multiobjective Optimization
Almost all approaches to multiobjective optimization are based on Genetic Algorithms, and implementations based on Evolution Strategies (ESs) are very rare. In this paper, a new approach to multiobjective optimization, based on ESs, is presented. The comparisons with other algorithms indicate a good performance of the Multiobjective Elitist
متن کاملA Note on Evolutionary Algorithms and Its Applications
This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.
متن کاملMulti-Objective Fault Section Estimation in Distribution Systems Using Elitist NSGA
In this paper, a non-dominated sorting based multi objective EA (MOEA), called Elitist non dominated sorting genetic algorithm (Elitist NSGA) has been presented for solving the fault section estimation problem in automated distribution systems, which alleviates the difficulties associated with conventional techniques of fault section estimation. Due to the presence of various conflicting object...
متن کاملA Fast Elitist Multiobjective Genetic Algorithm: Nsga-ii
NSGA ( [5]) is a popular non-domination based genetic algorithm for multiobjective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism...
متن کاملMultiobjective Genetic Based Algorithm
In this paper, a novel approach based on handling constraints as objectives together with a modified Parks & Miller elitist technique, to solve constrained multiobjective optimization problems, is analyzed with Niched Pareto Genetic Algorithm. The performance of this approach is compared with the classical procedure of handling constraints that is the exterior penalty function method. Results a...
متن کامل