Archivers for the representation of the set of approximate solutions for MOPs

In this paper we address the problem of computing suitable representations of the set of approximate solutions of a given multi-objective optimization problem via stochastic search algorithms. For this, we will propose different archiving strategies for the selection of the candidate solutions maint...

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Bibliographic Details
Published inJournal of heuristics Vol. 25; no. 1; pp. 71 - 105
Main Authors Schütze, O., Hernández, C., Talbi, E-G., Sun, J. Q., Naranjani, Y., Xiong, F.-R.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.02.2019
Springer Nature B.V
Springer Verlag
Subjects
Archiving
Artificial Intelligence
Calculus of Variations and Optimal Control; Optimization
Computer Science
Management Science
Mathematics
Mathematics and Statistics
Mopping
Multiple objective analysis
Operations Research
Operations Research/Decision Theory
Representations
Search algorithms
Search process
Stochastic models
Upper bounds
Stochastic search algorithm
Multi-objective optimization
Pareto set
Approximate solutions
Convergence
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Summary:In this paper we address the problem of computing suitable representations of the set of approximate solutions of a given multi-objective optimization problem via stochastic search algorithms. For this, we will propose different archiving strategies for the selection of the candidate solutions maintained by the generation process of the stochastic search process, and investigate them further on analytically and empirically. For all archivers we will provide upper bounds on the approximation quality as well as on the cardinality of the limit solution set. We conclude this work by a comparative study on some test problems in order to visualize the effect of all novel archiving strategies.
ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-018-9383-z