On the estimation of pareto front and dimensional similarity in many-objective evolutionary algorithm

Li, Li; Yen, Gary G.; Sahoo, Avimanyu; Chang, Liang; Gu, Tianlong

doi:10.1016/j.ins.2021.03.008

Cited by 54 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the grid-based evolutionary algorithm [29], grid ranking (GR) is for the convergence measuring, grid crowding distance (GCD) and grid coordinate point distance (GCPD) are utilized for diversity. In the algorithm on the base of estimation of pareto front and dimensional similarity (PeEA) [30], convergence parameter is L p -metric and diversity parameter is dimensionality margin distance (DMD).…”

Section: Reasonable Computing Resources Allocation Methodsmentioning

confidence: 99%

“…After individualistic and social actions, each individual that in the population is moved to the location of the element in the archive that best improves the corresponding sub-problem, unless that location is already occupied by another individual (line [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Finally, the archive A is the final results which is different from normal algorithms that return population as results.…”

Section: Defective Weight Vector Settingmentioning

confidence: 99%

See 1 more Smart Citation

Multi Agent Collaborative Search Many-objective Optimization Algorithm

Cao

Wang

Dai

et al. 2023

Preprint

View full text Add to dashboard Cite

MACS (Multi Agent Collaborative Search) is a creative multi-objective optimization algorithm which is effective for handling standard benchmarks and real engineering problems. It has a long improving history and the latest version is Improved archiving and search strategies for Multi Agent Collaborative Search (MACS2.1). But its ability on solving many-objective optimization problems is not good enough. This paper extends the original MACS2.1 to improve its performance to treat those problems and proposes a new multi agent collaborative search many-objective optimization algorithm (named Ma-MACS). Firstly, a more reasonable computing resources allocation approach (utility function) is applied to balance the quality for each individual. Secondly, a neighborhood updating process is embedded to increase evolution speed. Next, more mutation operators and related choosing strategy are applied to enhance the quality of the offspring. Finally, a weight vectors adjusting procedure is introduced to replace the inappropriate vectors. The new algorithm is compared with some state-of-art algorithms and MACS2.1 on the test cases.

show abstract

Section: Reasonable Computing Resources Allocation Methodsmentioning

confidence: 99%

Section: Defective Weight Vector Settingmentioning

confidence: 99%

Multi Agent Collaborative Search Many-objective Optimization Algorithm

Cao

Wang

Dai

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…(3) PeEA: Similar to SPEA2, this method is a posterior method to find the entire PF. The experiments in [45] show the performance of PeEA is better than SPEA2 on MaOPs. It is noted that there is no user-defined parameters in PeEA.…”

Section: Comparison Between ĝA-nscsa and Other Algorithmsmentioning

confidence: 98%

“…In this part, we compare the performances of ĝa-NSCSA and other popular and recent preference-based MOEAs, including the multiobjective evolutionary algorithm based ar-dominance (ar-MOEA) [20], strength Pareto evolutionary algorithm 2 (SPEA2) [44], Pareto front shape estimation based evolutionary algorithm (PeEA) [45], multiobjective evolutionary algorithm based on decomposition (MOEA/D) [7], and interactive reference vector guided evolutionary algorithm (iRVEA) [25]. All these algorithms adopt the reference point to describe the preference of the DM.…”

Section: Comparison Between ĝA-nscsa and Other Algorithmsmentioning

confidence: 99%

A novel dynamic reference point model for preference-based evolutionary multiobjective optimization

Lin

Luo

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

In the field of preference-based evolutionary multiobjective optimization, optimization algorithms are required to search for the Pareto optimal solutions preferred by the decision maker (DM). The reference point is a type of techniques that effectively describe the preferences of DM. So far, the reference point is either static or interactive with the evolutionary process. However, the existing reference point techniques do not cover all application scenarios. A novel case, i.e., the reference point changes over time due to the environment change, has not been considered. This paper focuses on the multiobjective optimization problems with dynamic preferences of the DM. First, we propose a change model of the reference point to simulate the change of the preference by the DM over time. Then, a dynamic preference-based multiobjective evolutionary algorithm framework with a clonal selection algorithm (ĝa-NSCSA) and a genetic algorithm (ĝa-NSGA-II) is designed to solve such kind of optimization problems. In addition, in terms of practical applications, the experiments on the portfolio optimization problems with the dynamic reference point model are tested. Experimental results on the benchmark problems and the practical applications show that ĝa-NSCSA exhibits better performance among the compared optimization algorithms.

show abstract

“…If no other solution dominates x , then x is a Pareto optimal solution [ 33 ]. The objective vectors corresponding to all Pareto optimal solutions constitute the Pareto optimal front (PF) [ 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

Feature Selection Based on a Large-Scale Many-Objective Evolutionary Algorithm

Sun

Liu

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The feature selection problem is a fundamental issue in many research fields. In this paper, the feature selection problem is regarded as an optimization problem and addressed by utilizing a large-scale many-objective evolutionary algorithm. Considering the number of selected features, accuracy, relevance, redundancy, interclass distance, and intraclass distance, a large-scale many-objective feature selection model is constructed. It is difficult to optimize the large-scale many-objective feature selection optimization problem by using the traditional evolutionary algorithms. Therefore, this paper proposes a modified vector angle-based large-scale many-objective evolutionary algorithm (MALSMEA). The proposed algorithm uses polynomial mutation based on variable grouping instead of naive polynomial mutation to improve the efficiency of solving large-scale problems. And a novel worst-case solution replacement strategy using shift-based density estimation is used to replace the poor solution of two individuals with similar search directions to enhance convergence. The experimental results show that MALSMEA is competitive and can effectively optimize the proposed model.

show abstract

On the estimation of pareto front and dimensional similarity in many-objective evolutionary algorithm

Cited by 54 publications

References 42 publications

Multi Agent Collaborative Search Many-objective Optimization Algorithm

Multi Agent Collaborative Search Many-objective Optimization Algorithm

A novel dynamic reference point model for preference-based evolutionary multiobjective optimization

Feature Selection Based on a Large-Scale Many-Objective Evolutionary Algorithm

Contact Info

Product

Resources

About