Feature Extraction for Recommendation of Constrained Multiobjective Evolutionary Algorithms

Qiao, Kangjia; Yu, Kunjie; Qu, Boyang; Yue, Caitong; Ban, Xuanxuan

doi:10.1109/tevc.2022.3186667

Cited by 13 publications

(3 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because using different operators will perform differently on the same problem. [43] Therefore, we have comprehensively selected the operator that performs best on this problem. When the binary crossover of probability simulation was set to 1, the probability of polynomial mutation was set to 1 / D (D represents the number of decision variables).…”

Section: B Cmoeas Used For Comparisonsmentioning

confidence: 99%

A Multistage Algorithm for Solving Multiobjective Optimization Problems With Multiconstraints

Sun

Zou

Liu

et al. 2023

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

There are usually multiple constraints in constrained multi-objective optimization. Those constraints reduce the feasible area of the constrained multi-objective optimization problems (CMOPs) and make it difficult for current multiobjective optimization algorithms (CMOEAs) to obtain satisfactory feasible solutions. In order to solve this problem, this paper studies the relationship between constraints, then obtains the priority between constraints according to the relationship between the Pareto Front (PF) of the single constraint and their common PF. Meanwhile, this paper proposes a multi-stage CMOEA and applies this priority, which can save computing resources while helping the algorithm converge. The proposed algorithm completely abandons the feasibility in the early stage to better explore the objective space, and obtains the priority of constraints according to the relationship; Then the algorithm evaluates a single constraint in the medium stage to further explore the objective space according to this priority, and abandons the evaluation of some less-important constraints according to the relationship to save the evaluation times; At the end stage of the algorithm, the feasibility will be fully considered to improve the quality of the solutions obtained in the first two stages, and finally get the solutions with good convergence, feasibility, and diversity. The results on five CMOP suites and three realworld CMOPs show that the algorithm proposed in this paper can have strong competitiveness in existing constrained multiobjective optimization.

show abstract

Section: B Cmoeas Used For Comparisonsmentioning

confidence: 99%

A Multistage Algorithm for Solving Multiobjective Optimization Problems With Multiconstraints

Sun

Zou

Liu

et al. 2023

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…Generally speaking, CHTs and the internal mechanism used by CMOEAs play a decisive role in the performance of the algorithms. From this perspective, CMOEAs can be divided into the following categories: (1) The penalty function method [12][13][14]; (2) The methods that consider constraints and objectives separately [15][16][17]; (3) the method of using two-stage [18][19][20]; and (4) the method based on dual-population [21][22][23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many optimization problems in the real world usually contain multiple objective functions and complex constraints, which can be collectively referred to as constrained multi-objective optimization problems (CMOPs) [1][2][3]. Generally, CMOPs can be defined by the following formula [4]:…”

Section: Introductionmentioning

confidence: 99%

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

Liang

Chen

Wang

et al. 2023

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and multiple conflicting objectives to be optimized. Therefore, the challage in addressing CMOPs is how to better balance constraints and objectives. To remedy this issue, this paper proposes a novel dual-population based constrained multi-objective evolutionary algorithm to solve CMOPs, in which two populations with different functions are employed. Specifically, the main population considers both objectives and constraints for solving the original CMOPs, while the auxiliary population is used only for optimization of objectives without considering constraints. In addition, a dynamic population size reducing mechanism is proposed, which is used to adjust the size of the auxiliary population, so as to reduce the consumption of computing resoruces in the later stage. Moreover, an independent external archive is set to store feasible solutions found by the auxiliary population, so as to provide high-quality feasible solutions for the main population. The experimental results on 55 benchmark functions show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms.

show abstract

A dual-population evolutionary algorithm based on dynamic constraint processing and resources allocation for constrained multi-objective optimization problems

Qiao,

Chen,

et al. 2024

Expert Systems with Applications

View full text Add to dashboard Cite

Feature Extraction for Recommendation of Constrained Multiobjective Evolutionary Algorithms

Cited by 13 publications

References 58 publications

A Multistage Algorithm for Solving Multiobjective Optimization Problems With Multiconstraints

A Multistage Algorithm for Solving Multiobjective Optimization Problems With Multiconstraints

A dual-population constrained multi-objective evolutionary algorithm with variable auxiliary population size

A dual-population evolutionary algorithm based on dynamic constraint processing and resources allocation for constrained multi-objective optimization problems

Contact Info

Product

Resources

About