Utilizing the Relationship Between Unconstrained and Constrained Pareto Fronts for Constrained Multiobjective Optimization

Liang, Jing; Qiao, Kangjia; Yu, Kunjie; Qu, Boyang; Yue, Caitong; Guo, Weifeng; Wang, Ling

doi:10.1109/tcyb.2022.3163759

Cited by 91 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, some excellent objectives information will be lost since the priority of the objectives is not greater than the constraints. Liang et al [32] explored the relationship between UPF and CPF at the learning stage, and then the relationship was used to guide the evolution of the population at the evolutionary stage. In general, the design of the two-stage mechanism is very prominent, and the transition conditions between stages should be reasonable, which will greatly affect the performance of the algorithm in solving CMOPs.…”

Section: Literature Reviewmentioning

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

Section: Literature Reviewmentioning

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

show abstract

“…In recent years, many swarm optimization algorithms has developed, such as hunger games search (HGS) [ 23 ], weighted mean of vectors (INFO) [ 24 ], Runge Kutta optimizer (RUN) [ 25 ], colony predation algorithm (CPA) [ 26 ], slime mould algorithm (SMA) [ 27 , 28 ], Harris hawks optimization (HHO) [ 29 ], and rime optimization algorithm (RIME) [ 30 ]. They have been applied to solve many problems such as bankruptcy prediction [ 31 ], global optimization [ 32 , 33 ], constrained multi-objective optimization [ 34 ], numerical optimization [ [35] , [36] , [37] ], scheduling optimization [ 38 , 39 ], large-scale complex optimization [ 40 ], feature selection [ [41] , [42] , [43] , [44] , [45] ], multi-objective optimization [ 46 ], economic emission dispatch [ 47 ], and feed-forward neural networks [ 48 ]. In addition to these, ant lion optimizer (ALO) was designed in 2015, which mainly inspired the predatory action of ant lions of nature, mainly including ants, ant lions and elite ant lions [ 22 ].…”

Section: Ant Lion Optimizermentioning

confidence: 99%

Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine

Huang

Xie

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…CMOPs involve optimizing multiple conflicting objectives subject to constraints that must be satisfied simultaneously, making it challenging to navigate towards the constrained Pareto front (CPF) [ 7 , 8 ] and obtain satisfactory solutions. Despite the numerous constrained multi-objective evolutionary algorithms (CMOEAs) proposed in the past two decades, only a few have been able to balance convergence, diversity, and feasibility, particularly when dealing with complex feasible regions.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems

Yang

Zhang

2023

Biomimetics

View full text Add to dashboard Cite

Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the ϵ-constraint handling method, with the ϵ value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.

show abstract

Utilizing the Relationship Between Unconstrained and Constrained Pareto Fronts for Constrained Multiobjective Optimization

Cited by 91 publications

References 34 publications

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

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

Prediction of fractional flow reserve with enhanced ant lion optimized support vector machine

A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems

Contact Info

Product

Resources

About