A tri-population based co-evolutionary framework for constrained multi-objective optimization problems

Ming, Fei; Gong, Wenyin; Lu, Chao

doi:10.1016/j.swevo.2022.101055

Cited by 50 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To show the superiority of CMOCPA, seven CMOEAs were selected: BiCo [ 29 ], C-TAEA [ 31 ], CAEAD [ 39 ], ICMA [ 40 ], PPS [ 30 ], ToP [ 41 ], and Trip [ 42 ]. To validate the performance of CMOCPA on ZDT [ 43 ], DTLZ [ 44 ], and RWMOPs [ 45 ], seven algorithms were used for comparison, including MOEAD [ 46 ], eMOEA [ 47 ], MOPSO [ 48 ], NSGAII [ 18 ], SPEA2 [ 33 ], KnEA [ 49 ], and GrEA [ 50 ].…”

Section: Simulation Experiments and Results Analysismentioning

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

Section: Simulation Experiments and Results Analysismentioning

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

show abstract

“…Terefore, on the basis of the research on the task confguration of UAVs carrying out combat reconnaissance tasks in naval battlefelds by using the particle swarm optimization algorithm, this paper fully considers the diferences between diferent UAVs' equipment, the diferences between diferent naval battlefelds' combat reconnaissance tasks, and the diferences between diferent UAVs carrying out diferent naval battlefelds' combat reconnaissance tasks of diferent nature [18]. Trough the research on such issues, it can efectively improve the efciency and efectiveness of UAVs in carrying out combat reconnaissance tasks in the complex naval battle environment [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization Algorithm

Dong,

Chen,

Wang

et al. 2024

Complexity

View full text Add to dashboard Cite

In recent years, in the classic battles and armed conflicts around the world, battlefield environment reconnaissance and the collection and processing of operational information play an increasingly critical role in the victory and defeat of the battlefield. Unmanned equipment, especially UAV equipment, is used by more and more countries in the field of combat reconnaissance. Meanwhile, the types of UAV are gradually diversified with the change of operational requirements. UAVs adapted to different combat environments shine brightly on the battlefield. In terms of naval battle field, due to the limitations and deficiencies of reconnaissance methods such as surface radar, UAVs play a more prominent role in combat reconnaissance. There are more scenarios for UAVs to be used in combat reconnaissance in naval battle field and higher requirements for UAVs’ combat effectiveness. Therefore, this paper takes UAVs’ naval battle reconnaissance missions as the research object. By using PSO as the research method, this paper studies the combat reconnaissance task configuration of UAVs, hoping to contribute to the improvement of UAVs’ combat reconnaissance capability and combat effectiveness.

show abstract

“…To further illustrate the performance of the proposed method, a comparison was conducted with CMOEA-MS [43], BiCo [44], AGE-MOEA-II [45], DSPCMDE [46], NSGA-II [17], IDCNSGA-III, Trip [47], DPPPS [47], and CAEAD [48]. The parameter values for the aforementioned comparative algorithms were kept consistent with the PlatEMO.…”

Section: Comparison Algorithms and Parameters Settingmentioning

confidence: 99%

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Zuo,

Xue

2024

Mathematics

View full text Add to dashboard Cite

Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and ε feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for ε constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

show abstract

A tri-population based co-evolutionary framework for constrained multi-objective optimization problems

Cited by 50 publications

References 40 publications

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

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

Research on Combat Mission Configuration of Unmanned Aerial Vehicle Maritime Reconnaissance Based on Particle Swarm Optimization Algorithm

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Contact Info

Product

Resources

About