An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints

Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms f...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 18; no. 4; pp. 577 - 601
Main Authors: Deb, Kalyanmoy, Jain, Himanshu
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.08.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Computer science; control theory; systems
Constraints
Educational institutions
Evolutionary
Evolutionary algorithms
evolutionary computation
Exact sciences and technology
Handling
large dimension
Many-objective optimization
Measurement
multi-criterion optimization
non-dominated sorting
NSGA-III
Optimization
Optimization algorithms
Production methods
Recognition
Sociology
Statistics
Theoretical computing
Vectors
Zirconium
large dimension
many-objective optimization
NSGA-III
nondominated sorting
multicriterion optimization
Evolutionary computation
Non dominating set
Large dimension
Multicriteria analysis
Evolutionary algorithm
Multiobjective programming
Optimization
Sorting
Problem solving
Mathematical programming
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Description
Summary:Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing need for developing evolutionary multiobjective optimization (EMO) algorithms for handling many-objective (having four or more objectives) optimization problems. In this paper, we recognize a few recent efforts and discuss a number of viable directions for developing a potential EMO algorithm for solving many-objective optimization problems. Thereafter, we suggest a reference-point-based many-objective evolutionary algorithm following NSGA-II framework (we call it NSGA-III) that emphasizes population members that are nondominated, yet close to a set of supplied reference points. The proposed NSGA-III is applied to a number of many-objective test problems with three to 15 objectives and compared with two versions of a recently suggested EMO algorithm (MOEA/D). While each of the two MOEA/D methods works well on different classes of problems, the proposed NSGA-III is found to produce satisfactory results on all problems considered in this paper. This paper presents results on unconstrained problems, and the sequel paper considers constrained and other specialties in handling many-objective optimization problems.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2013.2281535