A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II

DMCA

A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II (2000)

Cached

Download Links

by Kalyanmoy Deb , Samir Agrawal , Amrit Pratap , T Meyarivan

Citations:

661 - 15 self

BibTeX

@INPROCEEDINGS{Deb00afast,
    author = {Kalyanmoy Deb and Samir Agrawal and Amrit Pratap and T Meyarivan},
    title = {A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II},
    booktitle = {},
    year = {2000},
    pages = {849--858},
    publisher = {Springer}
}

OpenURL

Abstract

Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) -4 computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) solutions. Simulation results on five difficult test problems show that the proposed NSGA-II is able to find much better spread of solutions in all problems compared to PAES---another elitist multi-objective EA which pays special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach, NSGA-II should find increasing applications in the years to come.

Keyphrases

multi-objective optimization multi-objective evolutionary algorithm computational complexity non-dominated sorting parameter-less sharing approach mating pool difficult test problem special attention towards low computational requirement selection operator elitist multi-objective ea population size elitist approach diverse pareto-optimal front non-dominated sorting ga-ii sharing parameter non-elitism approach child population simulation result

DMCA