A Stable Large-Scale Multiobjective Optimization Algorithm with Two Alternative Optimization Methods.

Tianyu Liu, Junjie Zhu, Lei Cao

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

TS-SSA: An improved two-stage sparrow search algorithm for large-scale many-objective optimization problems.PloS one · 2025
Article

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Tianyu LiuCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.ORCID 0000-0003-0741-4743

Junjie ZhuCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Lei CaoCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Funding

the National Natural Science Foundation of China 61806122the National Natural Science Foundation of China 62102242

6 · The paper itself

Abstract

For large-scale multiobjective evolutionary algorithms based on the grouping of decision variables, the challenge is to design a stable grouping strategy to balance convergence and population diversity. This paper proposes a large-scale multiobjective optimization algorithm with two alternative optimization methods (LSMOEA-TM). In LSMOEA-TM, two alternative optimization methods, which adopt two grouping strategies to divide decision variables, are introduced to efficiently solve large-scale multiobjective optimization problems. Furthermore, this paper introduces a Bayesian-based parameter-adjusting strategy to reduce computational costs by optimizing the parameters in the proposed two alternative optimization methods. The proposed LSMOEA-TM and four efficient large-scale multiobjective evolutionary algorithms have been tested on a set of benchmark large-scale multiobjective problems, and the statistical results demonstrate the effectiveness of the proposed algorithm.

Indexed as

Bayesian-based parameter adjustingevolutionary algorithmslarge-scale multiobjective optimizationtwo alternative optimization methods

Identifiers

PMID37190349

DOI10.3390/e25040561

PMCPMC10137748

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.