Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization
Subtitle of host publication11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings
EditorsHisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou
Place of PublicationCham
PublisherSpringer
Pages605-616
ISBN (Electronic)9783030720629
ISBN (Print)9783030720612
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science
Volume12654
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021)
LocationHampton by Hilton Hotel (on-site & on-line)
PlaceChina
CityShenzhen
Period28 - 31 March 2021

Abstract

The main feature of large-scale multi-objective optimization problems (LSMOP) is to optimize multiple conflicting objectives while considering thousands of decision variables at the same time. Since the purpose of effective LSMOP algorithm is escaping from local optimum in large search space, the current research is focused on decision variable analysis or grouping, which easily leads to excessive computational complexity due to the large-scale decision variables. In order to maintain the diversity of the population while avoiding the computational complexity caused by large-scale decision variables, we propose a Probabilistic Prediction Model based on trend prediction model (TPM) and Generating-Filtering strategy to tackle LSMOP. Since TPM has an individual-based evolution mechanism, the computational complexity of the proposed algorithm is independent of decision variables, which maintains low complexity of the evolutionary algorithm while ensuring that the algorithm can converge to the Pareto optimal Front(POF). We compared the proposed algorithm with several state-of-the-art algorithms for different benchmark functions. The experimental results and complexity analysis have demonstrated that the proposed algorithm has significant improvement in terms of its performance and computational efficiency in large-scale multi-objective optimization.

Research Area(s)

  • Evolutionary multi-objective optimization, Large-scale optimization, Probabilistic prediction model, Trend prediction model

Citation Format(s)

Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model. / Hong, Haokai; Ye, Kai; Jiang, Min; Tan, Kay Chen.

Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. ed. / Hisao Ishibuchi; Qingfu Zhang; Ran Cheng; Ke Li; Hui Li; Handing Wang; Aimin Zhou. Cham : Springer, 2021. p. 605-616 (Lecture Notes in Computer Science; Vol. 12654).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review