MEANDS: A Many-objective Evolutionary Algorithm based on Non-dominated Decomposed Sets applied to multicast routing

[Display omitted] •MEANDS is proposed: Many-objective Evolutionary Algorithm based on Non-dominated Decomposed Sets.•MEANDS is tailored to deal with discrete optimization problems with many objectives.•MEANDS decomposes the original multi-objective problem (MOP) into simpler MOPs.•An environment bas...

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Bibliographic Details
Published in:Applied soft computing Vol. 62; pp. 851 - 866
Main Authors: Lafetá, Thiago, Bueno, Marcos L.P., Brasil, Christiane, Oliveira, Gina M.B.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2018
Subjects:
Evolutionary algorithms
Genetic algorithms
Many-objective algorithms
Metaheuristics
Multi-objective optimization
Multicast routing
Multicast routing
Multi-objective optimization
Evolutionary algorithms
Many-objective algorithms
Metaheuristics
Genetic algorithms
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:[Display omitted] •MEANDS is proposed: Many-objective Evolutionary Algorithm based on Non-dominated Decomposed Sets.•MEANDS is tailored to deal with discrete optimization problems with many objectives.•MEANDS decomposes the original multi-objective problem (MOP) into simpler MOPs.•An environment based on MEANDS is built to solve multicast routing problem (MRP).•The performance of MEANS was compared to six other MOEAs in MRP with 4–6 QoS objectives: MEAMT, SPEA2, SPEA2+SDE, MOEA/D, MOEA/DD and NSGA-III. Evolutionary algorithms have emerged in the last twenty years as a powerful approach for dealing with multi-objective optimization problems (MOPs). Although classical multi-objective evolutionary algorithms (MOEAs), such as SPEA2 and NSGA-II, have been designed to manipulate any number of objectives, the results of their practical application to MOPs with more than three objectives revealed that they have limitations. Many-objective algorithms represent the novelty in MOEA research because they are specially designed to handle search spaces of high dimension. In this paper, a new evolutionary algorithm able to handle discrete optimization problems with many objectives is proposed, called Many-objective Evolutionary Algorithm based on Non-dominated Decomposed Sets (MEANDS). MEANDS decomposes the original MOP into several, simpler MOPs, for which sub-populations of non-dominated solutions are maintained and evolved together. MEANDS relaxes several restrictions of predecessor algorithms, such as the size of sub-populations and the need for weights in the lower dimension MOPs. Empirical results show that MEANDS was able to find better solutions than those from well-known MOEAs (NSGA-III, SPEA2, SPEA2+SDE, MOEA/D, MOEA/DD, and its predecessor MEAMT) in the multicast routing problem involving 4, 5, and 6 QoS-based objectives.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2017.09.017