An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach

In the precursor paper, a many-objective optimization method (NSGA-III), based on the NSGA-II framework, was suggested and applied to a number of unconstrained test and practical problems with box constraints alone. In this paper, we extend NSGA-III to solve generic constrained many-objective optimi...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 18; no. 4; pp. 602 - 622
Main Authors:	Jain, Himanshu, Deb, Kalyanmoy
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:	Adaptive algorithms Algorithm design and analysis 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 Heuristic large dimension Many-objective optimization Measurement multi-criterion optimization non-dominated sorting NSGA-III Optimization Optimization algorithms Precursors Sociology Sorting Statistics Theoretical computing large dimension many-objective optimization NSGA-III nondominated sorting multicriterion optimization Evolutionary computation Constraint handling Non dominating set On the fly Large dimension Adaptive algorithm Dominating set Pareto optimum Multicriteria analysis Evolutionary algorithm Multiobjective programming Constraint satisfaction Updating Adaptive method Precursor Constrained optimization Sorting non-dominated sorting Genetic algorithm Problem solving Mathematical programming
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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