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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
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
Jiawei Yuan et al.
Summary: Research has shown that a mixture of feasible and infeasible solutions is beneficial for solving constrained multi-objective optimization problems, and the proposed criterion is more effective in identifying valuable infeasible solutions. The algorithm performs well in dealing with complex CMOPs and can successfully handle problems where the initial population is located in infeasible regions below the Pareto fronts.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Zhi-Hui Zhan et al.
Summary: This paper discusses the complex optimization problems brought by economic and social development, introduces the prospects and effectiveness of evolutionary computation algorithms in solving these problems, and proposes some future research directions.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Abhishek Gupta et al.
Summary: Evolutionary multitasking (EMT) is a concept that fills the potential gap of skill transfer between distinct optimization problems in evolutionary computation, by utilizing a population's implicit parallelism to jointly solve a set of tasks. This paper reviews various application-oriented explorations of EMT and provides recipes on how general problem formulations can be transformed in the light of EMT.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2022)
Article
Automation & Control Systems
Kunjie Yu et al.
Summary: In this article, a dynamic selection preference-assisted constrained multiobjective differential evolutionary algorithm is proposed to adjust the tradeoff between objective functions and constraints dynamically, yielding superior performance in solving constrained multiobjective optimization problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(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
Automation & Control Systems
Ke Chen et al.
Summary: This study proposes a novel PSO-based feature selection method to solve high-dimensional classification problems through information sharing between two related tasks, achieving higher classification accuracy in a faster time than existing methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Xiao-Fang Liu et al.
Summary: The neural network-based control method is a promising technique for controller design in power electronic circuits, but faces significant challenges in optimization. This article presents an evolutionary computation-based algorithm using the differential evolution algorithm and distributed differential evolution to enhance global optimization ability and reduce computational time, along with a resource-aware strategy to efficiently utilize computing resources. Experimental results show that this algorithm provides significantly better solutions in a shorter computational time compared to other typical evolutionary algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(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: In this paper, a hybrid algorithm is proposed to solve large-scale multi-objective optimization problems (LSMOPs) by combining differential evolution and conjugate gradient method. The proposed algorithm exhibits better convergence and diversity performance compared to existing evolutionary algorithms, mathematical programming methods, and hybrid algorithms on various benchmark and real-world LSMOPs.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Engineering, Civil
Shu-Zi Zhou et al.
Summary: The study introduces a new model for airline crew rostering problem that considers both fairness and satisfaction, and develops a multi-objective ACS algorithm. The algorithm uses two ant colonies to optimize fairness and satisfaction objectives, introduces a hybrid complementary heuristic strategy, and includes two types of local search strategies. Experimental results show that MOACS generally outperforms other algorithms, especially on large-scale instances.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Energy & Fuels
Jun Yao et al.
Summary: The novel multifactorial evolutionary algorithm (MFEA) is introduced to address production optimization problems by treating them as multiple distinct tasks in a multitasking environment, utilizing latent similarities among them to improve overall optimization performance.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(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
Computer Science, Artificial Intelligence
Jiang Min et al.
Summary: This paper explores the use of a smoothing norm objective penalty function for two-cardinality sparse constrained optimization problems, demonstrating its good properties and convergence in solving such problems. The proposed algorithm is able to find a satisfactory approximate optimal solution in a numerical example.
Article
Computer Science, Artificial Intelligence
Kunjie Yu et al.
Summary: The paper presents a purposedirected two-phase multiobjective differential evolution (PDTP-MDE) algorithm to solve constrained multiobjective optimization problems by balancing convergence, diversity, and feasibility. By dividing the evolution process into two sequential phases, the algorithm aims to achieve the goal of obtaining well convergence and diversity in feasible Pareto front solutions.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Review
Automation & Control Systems
Yicun Hua et al.
Summary: This paper provides a comprehensive survey of research on solving multi-objective optimization problems with irregular Pareto fronts, covering basic concepts, benchmark test problems, analysis of irregularity causes, real-world optimization problems, existing methodologies, representative algorithms, strengths, weaknesses, open challenges, and future directions.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(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
Construction & Building Technology
Jiepeng Liu et al.
Summary: This paper focuses on automatically designing collision-free rebar layout in Reinforced Concrete (RC) structures, utilizing Particle Swarm Optimization (PSO), Differential Evolution (DE), and Neighborhood Field Optimization (NFO) algorithms to complete subtasks. Experimental results show that the decomposed optimization is effective for automatic rebar layout, with the PSO algorithm performing the best.
AUTOMATION IN CONSTRUCTION
(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
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.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Hui Wang et al.
Summary: This study proposes a new method for handling constrained multiobjective optimization problems by balancing minimizing objectives, satisfying constraints, and avoiding population getting stuck at locally optimal regions. Experimental results demonstrate the competitiveness of the proposed algorithm compared to existing state-of-art constrained evolutionary multiobjective optimization methods.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Fei Ming et al.
Summary: The paper proposes a simple and generic two-stage framework for handling constrained multi-objective optimization problems (CMOPs) to achieve better efficiency and versatility. The framework is divided into two stages focusing on approaching the unconstrained Pareto front and obtaining the constrained Pareto front of CMOPs. Through evaluating 57 instances in five benchmark test suites, the superiority or at least competitiveness of the framework is demonstrated.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Zhongwei Ma et al.
Summary: We propose a new constraint-handling technique based on a weighted sum of rankings using constrained dominance principle and Pareto dominance, which adaptively balances constraints and objectives in evolutionary optimization process. Experimental results show that our technique outperforms other representative constraint-handling techniques.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Lianghao Li et al.
Summary: The proposed pioneer selection strategy effectively handles complex constrained optimization problems with discontinuous feasible regions, by adjusting the ratio of pioneer solutions to approximate the Pareto optimal front. Experimental results demonstrate the effectiveness of the strategy and show that the proposed benchmark problems are challenging for existing approaches.
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(2020)
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ENERGY CONVERSION AND MANAGEMENT
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IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2020)
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(2019)
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(2019)
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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
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2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2019)
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(2019)
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(2018)
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(2012)
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(2002)