PEA: Parallel Evolutionary Algorithm by Separating Convergence and Diversity for Large-Scale Multi-Objective Optimization

Chen, Huangke; Zhu, Xiaomin; Pedrycz, Witold; Yin, Shu; Wu, Guohua; Yan, Hui

doi:10.1109/icdcs.2018.00031

Cited by 24 publications

(9 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are also some attempts to customize search operators to improve the scalability of certain MOEAs [96,97] . The work in [98] focuses on a specific type of large-scale problems named large-scale sparse MOPs, where most decision variables of the optimal solutions are zero. To deal with this kind of problem and improve the sparsity of the generated solutions, a new population initialization strategy and genetic operators by taking the sparse nature of the Pareto optimal solutions into consideration are proposed.…”

Section: Enhanced Search-based Large-scale Moeasmentioning

confidence: 99%

Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

Hong

Yang

Tang

2021

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Large-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs, most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables. More recently, it has been reported that traditional multi-objective EAs (MOEAs) suffer severe deterioration with the increase of decision variables. As a result, and motivated by the emergence of real-world large-scale MOPs, investigation of MOEAs in this aspect has attracted much more attention in the past decade. This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles. From the key difficulties of the large-scale MOPs, the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables. From the perspective of methodology, the large-scale MOEAs are categorized into three classes and introduced respectively: divide and conquer based, dimensionality reduction based and enhanced search-based approaches. Several future research directions are also discussed.

show abstract

Section: Enhanced Search-based Large-scale Moeasmentioning

confidence: 99%

Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

Hong

Yang

Tang

2021

Int. J. Autom. Comput.

View full text Add to dashboard Cite

show abstract

“…Then, different optimization strategies are adopted in the two groups to focus on the convergence and diversity, respectively. Recently, several parallelization techniques have been utilized in the grouping of decision variables to speed up the search process [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization

Huang

Yang

et al. 2022

IEEE Trans. Cybern.

201

View full text Add to dashboard Cite

This paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and manyobjective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems.

show abstract

“…In contrast, the diversity-related variables help LSMOEAs find the solution sets with a better distribution. Existing decision-variable grouping strategies can be divided into fixed grouping strategies [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] and dynamic grouping strategies [ 21 , 22 , 23 , 24 ]. In a fixed grouping strategy, the grouping results do not change during the evolution process, i.e., the evolutionary algorithm for large-scale many-objective optimization, LMEA.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Zhu

Cao

2023

Entropy

View full text Add to dashboard Cite

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.

show abstract

PEA: Parallel Evolutionary Algorithm by Separating Convergence and Diversity for Large-Scale Multi-Objective Optimization

Cited by 24 publications

References 36 publications

Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization

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

Contact Info

Product

Resources

About