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Article
Automation & Control Systems
Yi Xiang et al.
Summary: Constrained multiobjective optimization problems are common in real-world applications, and achieving a balance between constraints and objectives is challenging. This article proposes a new constraint handling technique that considers the potential problem types, and experimental results demonstrate its effectiveness in achieving a good tradeoff among different problem types.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Kangjia Qiao et al.
Summary: This paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm to better solve constrained multi-objective optimization problems (CMOPs). DBEMTO evolves two populations to solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP) respectively and uses three evolutionary strategies for offspring generation. DBEMTO has performed more competitively compared to other state-of-the-art CMOEAs according to the final results.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Jing Liang et al.
Summary: This article explores and utilizes the relationship between constrained Pareto front (CPF) and unconstrained Pareto front (UPF) to solve constrained multiobjective optimization problems (CMOPs). A new constrained multiobjective evolutionary algorithm (CMOEA) is presented by dividing the evolutionary process into learning stage and evolving stage. Experimental results show that the proposed method has better performance compared to state-of-the-art CMOEAs, indicating the promising use of the relationship between CPF and UPF in solving CMOPs.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhichao Sun et al.
Summary: In this article, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm (CRS-DE) is proposed to solve Highly Constrained Multiobjective Optimization Problems (HCMOPs). The algorithm relaxes the constraints by dividing the infeasible solutions into semifeasible subpopulation (SF) and infeasible subpopulation (IF), and devises corresponding reproduction and selection strategies for SF, IF, and feasible subpopulations. To prevent premature convergence, a mobility restriction mechanism is developed to restrict the individuals in SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Juan Zou et al.
Summary: The core element in solving CMOPs is to balance objective optimization and constraint satisfaction. We propose a flexible two-stage evolutionary algorithm based on automatic regulation (ARCMO) to adapt to complex CMOPs.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Kangjia Qiao et al.
Summary: This article presents an evolutionary multitasking-based constrained multiobjective optimization framework for solving CMOPs. It transforms the optimization problem into two related tasks and utilizes a tentative method to discover and transfer useful knowledge. The approach achieves better performance compared to other state-of-the-art algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Jun Dong et al.
Summary: One of the key issues in solving constrained multi-objective optimization problems is balancing convergence, diversity, and feasibility. This paper proposes a two-stage constrained multi-objective evolutionary algorithm with different emphases on the three indicators. Experimental results demonstrate that the proposed algorithm achieves significant improvements on most benchmark problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Christian L. Camacho-Villalon et al.
Summary: This paper proposes the use of automatic design to overcome the limitations of manually designing PSO algorithms. They develop a flexible software framework called PSO-X, which integrates the automatic configuration tool irace to select and configure high-performing PSO algorithms from a large number of algorithm components.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Xue Feng et al.
Summary: Balancing convergence and diversity is a challenge in multi-objective optimization problems, especially when the proportion of feasible regions is low. This paper proposes a constrained multi-objective optimization algorithm based on a hybrid driven strategy to enhance the feasibility and diversity performance of Pareto solutions. The algorithm outperforms peer algorithms, especially in large-infeasible-regions multi-objective optimization problems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Aljosa Vodopija et al.
Summary: This article extends landscape analysis to constrained multiobjective optimization and proposes a method for characterizing CMOPs. The method is used to compare artificial test suites and real-world problem suites, revealing the limitations of artificial test problems in representing realistic characteristics. The effectiveness of the proposed features in predicting algorithm performance is demonstrated.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Jiahai Wang et al.
Summary: This article proposes a cooperative multiobjective evolutionary algorithm with propulsive population (CMOEA-PP) to solve constrained multiobjective optimization problems (CMOPs), balancing convergence, diversity, and feasibility through the collaboration between propulsive population and normal population. Propulsive population focuses on convergence, while normal population prioritizes feasibility and must maintain diversity.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fei Ming et al.
Summary: This paper proposes a tri-population based co-evolutionary framework (TriP) to handle complex CMOPs. The experiments show that the proposed framework has competitive performance and versatility, and it is also effective in handling real-world CMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
M. Sri Srinivasa Raju et al.
Summary: The existence of constrained multi-objective optimization problems (CMOPs) has led researchers to develop constrained multi-objective evolutionary algorithms (CMOEAs). In order to handle CMOPs with discontinuous feasible regions or infeasible barriers, a novel Dual-Population and Multi-Stage based Constrained Multi-objective Evolutionary Algorithm (CMOEA-DPMS) is proposed, along with a new constraint handling technique (CHT) called decomposition based constraint non-dominating sorting (DCDSort) to maintain feasibility, convergence, and diversity.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jing-Yu Ji et al.
Summary: Constrained multiobjective optimization problems are commonly encountered in real-world applications. This study proposes an improved e-constrained multiobjective differential evolution algorithm to address these problems and demonstrates its superior performance on benchmark test functions.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Zhi-Zhong Liu et al.
Summary: This article presents a novel constrained multiobjective optimization algorithm BiCo, which maintains two populations and evolves from the feasible and infeasible regions towards the PF successfully.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Ye Tian et al.
Summary: The proposed two-stage evolutionary algorithm adjusts the balance between objective optimization and constraint satisfaction adaptively, addressing the difficulty of striking a good balance in complex feasible regions. Experimental studies demonstrate the superiority of the algorithm over state-of-the-art algorithms, especially on problems with complex feasible regions.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Yongkuan Yang et al.
Summary: Many domination-based multi-objective evolutionary algorithms are designed for constrained multi-objective optimization problems, but balancing feasibility, convergence, and distribution remains a challenge. The proposed MODE-CHS algorithm addresses this issue by using constraint-handling switching to enhance population convergence and obtain feasible solutions, while also involving an external archive and offspring in the evolution process to improve distribution. Experimental results demonstrate that MODE-CHS is competitive in solving CMOPs compared to other state-of-the-art algorithms.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yuren Zhou et al.
Summary: The article proposes a new framework for constructing constrained test problems, which introduces convergence-hardness and diversity-hardness constraints. It constructs 16 scalable and constrained test problems, evaluates the performance of existing algorithms, and shows that the problems are challenging.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Ye Tian et al.
Summary: This article proposes a coevolutionary framework for constrained multiobjective optimization problems, which demonstrates high competitiveness in experiments compared to other algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Chaoda Peng et al.
Summary: This article introduces a set of CMOPs with deceptive constraints and proposes a cooperative framework that effectively solves this problem. The framework consists of two phases, one for exploring feasible regions and the other for exploring the entire space, with the ability to switch phases based on information found during the evolutionary process.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Ruwang Jiao et al.
Summary: This study introduces a problem transformation technique to handle both constraints and optimize objectives simultaneously in constrained many-objective optimization problems (CMaOPs). The well-known reference-point-based NSGA-III is tailored under the problem transformation model as DCNSGA-III to solve CMaOPs effectively. Through the design of a mating selection mechanism and an environmental selection operator, the proposed algorithm generates and selects high-quality epsilon-feasible offspring solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Lourdes Uribe et al.
Summary: The study proposes an effective method for computing multi-objective descent directions without the need for explicit gradient computation, obtaining the direction by extracting information from the current population of the MOEA. Two hybrid methods focused on specific types of problems are demonstrated, with numerical results on benchmark problems supporting the benefits of the novel approach.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Abhishek Kumar et al.
Summary: The article introduces a new benchmark suite RWCMOPs for assessing the performance of Constrained Multi-objective Metaheuristics, consisting of 50 real-world problems and proposing a ranking scheme for comparative analysis.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Cyril Picard et al.
Summary: The article presents a framework called MODAct for designing electro-mechanical actuators and derives 20 constrained multiobjective optimization test problems from it. The effects of constraints on the Pareto front and convergence performance are analyzed, and a constraint landscape analysis approach with three new metrics is utilized to characterize the search and objective spaces. Comparison of MODAct features with existing test suites highlights differences and suggests that design problems are challenging due to constraints.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Mengjun Ming et al.
Summary: The study introduces a dual-population-based evolutionary algorithm, c-DPEA, for constrained multiobjective optimization problems (CMOPs), which achieves a balance between convergence and diversity through the design of novel self-adaptive penalty and fitness functions. Extensive experiments demonstrate the superiority of c-DPEA over six state-of-the-art CMOEAs on most test problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Bing-Chuan Wang et al.
Summary: The paper proposes an adaptive fuzzy penalty method to address the issue of tuning the penalty coefficient in constrained evolutionary optimization, adjusting the coefficient at both individual and population levels. By using differential evolution to design a search algorithm, the constrained optimization evolutionary algorithm AFPDE is proposed, showing competitiveness through experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Haiping Ma et al.
Summary: Constrained multi-objective optimization problems are challenging to handle due to the complexities of objectives and constraints. To address this issue, a multi-stage evolutionary algorithm is proposed in this paper, which gradually adds constraints and sorts their handling priority based on their impact on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art algorithms in dealing with complex constraint problems.
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(2021)
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