A New Many-Objective Evolutionary Algorithm Based on Generalized Pareto Dominance

Zhu, Shuwei; Xu, Lihong; Goodman, Erik D.; Lu, Zhichao

doi:10.1109/tcyb.2021.3051078

Cited by 47 publications

(17 citation statements)

References 69 publications

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“…• NSGA-III; 21 • MultiGPO; 41 • θ-DEA; 44 • MOEADDE; 45 • IBEA; 18 • RVEA; 34 • KnEA; 46 • Proposed TS-DGPD. All algorithms are tested on the PlatEMO platform.…”

Section: Experimental Designmentioning

confidence: 99%

“…Based on GPO, a new MaOEA (called MultiGPO) were proposed, 41 in which a “( M − 1) + 1” framework of GPO dominance (called (

M

‐1)‐GPD) is employed. In (

M

‐1)‐GPD, the dominance region is expanded on

M-1

objectives, and only one particular objective of the dominance region is not expanded.…”

Section: Preliminary and Related Workmentioning

confidence: 99%

“…To assess the comprehensive performance of TS‐DGPD, some latest MaOEAs are selected for comparisons. The compared MaOEAs are listed as follows: NSGA‐III; 21 MultiGPO; 41

\theta

‐DEA; 44 MOEADDE; 45 IBEA; 18 RVEA; 34 KnEA; 46 Proposed TS‐DGPD.…”

Section: Experiments Studymentioning

confidence: 99%

See 2 more Smart Citations

A two‐stage many‐objective evolutionary algorithm with dynamic generalized Pareto dominance

Wang

Wei

et al. 2022

Int J of Intelligent Sys

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Many-objective evolutionary algorithms (MaOEAs) are widely used to solve many-objective optimization problems. As the number of objectives increases, it is difficult to achieve a balance between the population diversity and the convergence. Additionally, the selection pressure decreases rapidly. To tackle these issues, this paper proposes a two-stage manyobjective evolutionary algorithm with dynamic generalized Pareto dominance (called TS-DGPD). First, a two-stage method is utilized for environmental selection. The first stage employs the cosine distance to accelerate the convergence. The second stage uses L p -norm maintain the population diversity.Moreover, a dynamic generalized Pareto dominance (DGPD) is used to increase the selection pressure of the population. To evaluate the performance of TS-DGPD, we compare it with several other MaOEAs on two benchmark sets with 3, 5, 8, 10, 15, and 20 objectives. Experimental results show that TS-DGPO performs satisfactorily on convergence and diversity.

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“…• NSGA-III; 21 • MultiGPO; 41 • θ-DEA; 44 • MOEADDE; 45 • IBEA; 18 • RVEA; 34 • KnEA; 46 • Proposed TS-DGPD. All algorithms are tested on the PlatEMO platform.…”

Section: Experimental Designmentioning

confidence: 99%

“…Based on GPO, a new MaOEA (called MultiGPO) were proposed, 41 in which a “( M − 1) + 1” framework of GPO dominance (called (

M

‐1)‐GPD) is employed. In (

M

‐1)‐GPD, the dominance region is expanded on

M-1

objectives, and only one particular objective of the dominance region is not expanded.…”

Section: Preliminary and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A two‐stage many‐objective evolutionary algorithm with dynamic generalized Pareto dominance

Wang

Wei

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…The second category is multi-objective optimization algorithms based on performance indicators, which select individuals based on the calculated performance indicator values. Common performance indicators include HV [9], IGD [10], R2 [11] etc. However, as the number of objectives increases, the computational cost of the HV [9] indicator increases exponentially.…”

Section: Introductionmentioning

confidence: 99%

High-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and improved association methods

Wang,

Xiang

et al. 2024

Preprint

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The balance between convergence and diversity is a crucial and challenging aspect of evolutionary multi-objective optimization. In order to balance them, this paper proposes a high-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and an improved association method (MOEAAP). An adaptive penalty parameter is first proposed and utilized in non-dominated sorting based on reference points, allowing for the dynamic assignment of Pareto ranks to the population according to changes in iteration counts and objective dimensions. Additionally, an improved association method is employed to categorize candidate solutions into associated and non-associated solutions, while associated solutions are given preferential consideration. To verify the feasibility of the proposed algorithm, it is compared with several mainstream algorithms through simulation experiments, and is applied to multiple practical applications. Experimental results show the algorithm effectively balances convergence and diversity in high-dimensional, complex multi-objective optimization.

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“…Although the above algorithm can accelerate the convergence of the population, the improved dominance relationship will lead to the deterioration of the diversity of the population [15]. At present, several new dominance relations have been developed, such as SDR [16], CSDR [17], and MultiGPO [18].…”

Section: Introductionmentioning

confidence: 99%

A Many-Objective Evolutionary Algorithm Based on Dual Selection Strategy

Cheng

Dai

Xue

2023

Entropy

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In high-dimensional space, most multi-objective optimization algorithms encounter difficulties in solving many-objective optimization problems because they cannot balance convergence and diversity. As the number of objectives increases, the non-dominated solutions become difficult to distinguish while challenging the assessment of diversity in high-dimensional objective space. To reduce selection pressure and improve diversity, this article proposes a many-objective evolutionary algorithm based on dual selection strategy (MaOEA/DS). First, a new distance function is designed as an effective distance metric. Then, based distance function, a point crowding-degree (PC) strategy, is proposed to further enhance the algorithm’s ability to distinguish superior solutions in population. Finally, a dual selection strategy is proposed. In the first selection, the individuals with the best convergence are selected from the top few individuals with good diversity in the population, focusing on population convergence. In the second selection, the PC strategy is used to further select individuals with larger crowding distance values, emphasizing population diversity. To extensively evaluate the performance of the algorithm, this paper compares the proposed algorithm with several state-of-the-art algorithms. The experimental results show that MaOEA/DS outperforms other comparison algorithms in overall performance, indicating the effectiveness of the proposed algorithm.

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A New Many-Objective Evolutionary Algorithm Based on Generalized Pareto Dominance

Cited by 47 publications

References 69 publications

A two‐stage many‐objective evolutionary algorithm with dynamic generalized Pareto dominance

A two‐stage many‐objective evolutionary algorithm with dynamic generalized Pareto dominance

High-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and improved association methods

A Many-Objective Evolutionary Algorithm Based on Dual Selection Strategy

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