Finding top-K solutions for the decision-maker in multiobjective optimization

Wenjian LUO, Luming SHI, Xin LIN, Jiajia ZHANG, Miqing LI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)

Abstract

Multiobjective optimization problems (MOPs) are the optimization problem with multiple conflicting objectives. Generally, an optimization algorithm can find a large number of optimal solutions for MOPs, which easily overwhelm decision makers (DMs) and make it difficult for decision-making. Preference-based evolutionary multiobjective optimization (EMO) aims to find the partial optima in the regions preferred by the DM. Although it narrows the scope of the optimal solutions, it usually still returns a population of optimal solutions (typically 100 or larger in EMO) with a small distance between adjacent optima. Top-K, which is a well-established research subject in many fields to find the best K solutions, may be a direction to reduce the number of optimal solutions. In this paper, first, we introduce the top-K notion into preference-based EMO and propose the top-K model to obtain the best K individuals of multiobjective optimization problems (MOPs). Then, with the top-K model, we propose NSGA-II-TopK and SPEA2-TopK to search for the top-K preferred solutions for preference-based continuous and combinatorial MOPs, respectively. Finally, the proposed algorithms with several representative preference-based EMO algorithms are compared in different preference situations for MOPs. Experimental results show the proposed algorithms have strong performances against the compared algorithms. © 2022 Elsevier Inc.
Original languageEnglish
Pages (from-to)204-227
Number of pages24
JournalInformation Sciences
Volume613
Early online date14 Sept 2022
DOIs
Publication statusPublished - Oct 2022
Externally publishedYes

Bibliographical note

This study is supported by the National Natural Science Foundation of China (No. 61573327), EPSRC (Grant Nos. EP/J017515/1 and EP/P005578/1), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. ZDSYS201703031748284) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).

Keywords

  • Evolutionary algorithm
  • Multi-objective optimization
  • Preference handling

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