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

Jain, Himanshu ; Deb, Kalyanmoy

IEEE transactions on evolutionary computation, 2014-08, Vol.18 (4), p.602-622 [Periódico revisado por pares]

New York, NY: IEEE

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Título:
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
Autor: Jain, Himanshu ; Deb, Kalyanmoy
Assuntos: 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
É parte de: IEEE transactions on evolutionary computation, 2014-08, Vol.18 (4), p.602-622
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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Descrição: 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.
Editor: New York, NY: IEEE
Idioma: Inglês

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

Jain, Himanshu ; Deb, Kalyanmoy

New York, NY: IEEE

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