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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2014-08, Vol.18 (4), p.602-622
Main Authors: Jain, Himanshu, Deb, Kalyanmoy
Format: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 optimization problems. In the process, we also suggest three types of constrained test problems that are scalable to any number of objectives and provide different types of challenges to a many-objective optimizer. A previously suggested MOEA/D algorithm is also extended to solve constrained problems. Results using constrained NSGA-III and constrained MOEA/D show an edge of the former, particularly in solving problems with a large number of objectives. Furthermore, the NSGA-III algorithm is made adaptive in updating and including new reference points on the fly. The resulting adaptive NSGA-III is shown to provide a denser representation of the Pareto-optimal front, compared to the original NSGA-III with an identical computational effort. This, and the original NSGA-III paper, together suggest and amply test a viable evolutionary many-objective optimization algorithm for handling constrained and unconstrained problems. These studies should encourage researchers to use and pay further attention in evolutionary many-objective optimization.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2013.2281534