NDLI: Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations

Content Provider	IEEE Xplore Digital Library
Author	Ishibuchi, H. Sakane, Y. Tsukamoto, N. Nojima, Y.
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Gakuen-cho, Naka-ku, 1-1, Sakai, Osaka, 599-8534 Japan (Ishibuchi, H.; Sakane, Y.; Tsukamoto, N.; Nojima, Y.)
Abstract	Evolutionary multiobjective optimization (EMO) is an active research area in the field of evolutionary computation. EMO algorithms are designed to find a non-dominated solution set that approximates the entire Pareto front of a multiobjective optimization problem. Whereas EMO algorithms usually work well on two-objective and three-objective problems, their search ability is degraded by the increase in the number of objectives. One difficulty in the handling of many-objective problems is the exponential increase in the number of non-dominated solutions necessary for approximating the entire Pareto front. A simple countermeasure to this difficulty is to use large populations in EMO algorithms. In this paper, we examine the behavior of EMO algorithms with large populations (e.g., with 10,000 individuals) through computational experiments on multiobjective and many-objective knapsack problems with two, four, six, eight and ten objectives. We examine two totally different algorithms: NSGA-II and MOEA/D. NSGA-II is a Pareto dominance-based algorithm while MOEA/D uses scalarizing functions. Their search ability is examined for various specifications of the population size under the fixed computation load. That is, we use the total number of examined solutions as the stopping condition of each algorithm. Thus the use of a very large population leads to the termination at an early generation (e.g., 20th generation). It is demonstrated through computational experiments that the use of too large populations makes NSGA-II very slow and inefficient. On the other hand, MOEA/D works well even when it is executed with a very large population. We also discuss why MOEA/D works well even when the population size is unusually large.
Starting Page	1758
Ending Page	1763
File Size	314847
Page Count	6
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346628
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Degradation Algorithm design and analysis Design optimization Pareto optimization Cybernetics USA Councils Computer science Intelligent systems Evolutionary computation Genetic algorithms cellular genetic algorithms Evolutionary multiobjective optimization (EMO) evolutionary many-objective optimization NSGA-II MOEA/D
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Review on Real Coded Genetic Algorithms Used in Multiobjective Optimization

Multiobjective evolutionary algorithm MOEA to solve task allocation problems in multi agents systems

Multiobjective evolutionary algorithm MOEA an approach for solving MAS multiatribute allocation task

Approximating a multi-dimensional Pareto front for a land use management problem: A modified MOEA with an epigenetic silencing metaphor

Incorporation of decision maker’s preference into evolutionary multiobjective optimization algorithms.

Optimization of scalarizing functions through evolutionary multiobjective optimization

Multiobjective Optimization with Messy Genetic Algorithms (2000)

Group Counseling Optimization for multi-objective functions

Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II

Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations

Similar Documents

Review on Real Coded Genetic Algorithms Used in Multiobjective Optimization

Multiobjective evolutionary algorithm MOEA to solve task allocation problems in multi agents systems

Multiobjective evolutionary algorithm MOEA an approach for solving MAS multiatribute allocation task

Approximating a multi-dimensional Pareto front for a land use management problem: A modified MOEA with an epigenetic silencing metaphor

Incorporation of decision maker’s preference into evolutionary multiobjective optimization algorithms.

Optimization of scalarizing functions through evolutionary multiobjective optimization

Multiobjective Optimization with Messy Genetic Algorithms (2000)

Group Counseling Optimization for multi-objective functions

Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II

Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations