Integration of improved predictive model and adaptive differential evolution based dynamic multi-objective evolutionary optimization algorithm

A novel dynamic multi-objective optimization evolutionary algorithm is proposed in this paper to track the Pareto-optimal set of time-changing multi-objective optimization problems. In the proposed algorithm, to initialize the new population when a change is detected, a modified prediction model uti...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 43; H. 1; S. 192 - 207
Hauptverfasser: Liu, Ruochen, Fan, Jing, Jiao, Licheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2015
Springer Nature B.V
Schlagworte:
Antibodies
Artificial Intelligence
Computer Science
Genetic algorithms
Immune system
Machines
Manufacturing
Mechanical Engineering
Mutation
Optimization algorithms
Optimization techniques
Pareto optimum
Performance evaluation
Population
Processes
Dynamic multiobjective optimization
Forecasting model
Self adaptive differential evolution
ISSN:0924-669X, 1573-7497
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Zusammenfassung:A novel dynamic multi-objective optimization evolutionary algorithm is proposed in this paper to track the Pareto-optimal set of time-changing multi-objective optimization problems. In the proposed algorithm, to initialize the new population when a change is detected, a modified prediction model utilizng the historical optimal sets obtained in the last two times is adopted. Meantime, to improve both convergence and diversity, a self-adaptive differential evolution crossover operator is used. We conducted two experiments: the first one compares the proposed algorithm with the other three dynamic multiobjective evolutionary algorithms, and the second one investigates the performance of the two proposed operators. The statistical results indicate that the proposed algorithm has better conergence speed and diversity and it is very promising for dealing with dynamic environment.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-014-0625-y