ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm

This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 127; pp. 65 - 77
Main Authors: Venske, Sandra M., Gonçalves, Richard A., Delgado, Myriam R.
Format: Journal Article Conference Proceeding
Language:English
Published: Amsterdam Elsevier B.V 15.03.2014
Elsevier
Subjects:
Adaptive algorithms
Adaptive differential evolution
Adaptive pursuit
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Computer science; control theory; systems
Credit assignment
Decision theory. Utility theory
Evolutionary algorithms
Exact sciences and technology
Matching
Mathematical programming
Multiobjective optimization
Operational research and scientific management
Operational research. Management science
Optimization
Probability matching
State of the art
Statistical tests
Strategy
Theoretical computing
Credit assignment
Probability matching
Adaptive pursuit
Multiobjective optimization
Adaptive differential evolution
Information credibility
Probabilistic approach
Pareto optimum
Evolutionary algorithm
Reinforcement learning
Multiobjective programming
Conceptual analysis
Adaptive method
Optimization
Statistical test
Semantics
Greedy algorithm
Differential evolution
Version management
Mathematical programming
ISSN:0925-2312, 1872-8286
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work we test two methods to perform adaptive strategy selection: Probability Matching (PM) and Adaptive Pursuit (AP). PM and AP are analyzed in combination with four credit assignment techniques based on relative fitness improvements. The DE strategy is chosen from a candidate pool according to a probability that depends on its previous experience in generating promising solutions. In experiments, we evaluate certain features of the proposed approach, considering eight different versions while solving a well established set of 10 instances of Multiobjective Optimization Problems. Next the best-so-far version (ADEMO/D) is confronted with its non-adaptive counterparts. Finally ADEMO/D is compared with four important multiobjective optimization algorithms in the same application context. Pareto compliant indicators and statistical tests are applied to evaluate the algorithm performances. The preliminary results are very promising and stand ADEMO/D as a candidate to the state-of-the-art for multiobjective optimization. •An approach for continuous multiobjective optimization is presented in this paper.•It incorporates concepts of multiobjective evolutionary algorithms based on decomposition and mechanisms of mutation strategies adaptation.•Two methods for adaptive strategy selection are analyzed in combination with four credit assignment techniques based on relative fitness improvements.•Different versions of the proposed approach are compared and the best-so-far version is confronted with methods of state-of-the-art.•The effectiveness of the proposed approach is demonstrated on 10 instances of a multiobjective optimization benchmark.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2013.06.043