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14th International Conference on Swarm Intelligence (ICSI)

作者： Qiao, Liyan Hou, Sibo Harbin Inst Technol Dept Automat Test & Control Harbin Peoples R China

ISBN: (纸本)9783031366215;9783031366222

constrained multi-objective optimization is facing huge challenges due to the complex constraints and the restraint of multiple optimization objectives. The most difficult problem need to solve is to achieve the balance between optimization objective convergence and feasibility under constraints as far as possible. To solve this problem, a constrained multi-objective particle swarm optimization algorithm based on co-evolution and gaining-sharing knowledge mechanism is proposed, which is called CCGSK-PSO. Based on the evolutionary strategy of co-evolution, the evolutionary process conducts two types of co-evolutionary in each iteration: the co-evolution of unconstrained evolutionary population and constrained evolutionary population, and the co-evolution of objective convergence subpopulation and constrained subpopulation in constrained evolutionary population. The objective convergence sub-population is mainly responsible for exploring the objective convergence boundary information, while learning from the individuals in constrained subpopulation in the neighborhood to guide the population to effectively cross the infeasible regions. The constrained subpopulation is mainly responsible for exploring the constraint boundary information, while guiding the population to converge to the elite individuals. Finally, the obtained solution set is widely and uniformly distributed along the constrained Pareto front. The experimental results on 37 test problems in three benchmark suites show the effectiveness of CCGSK-PSO.

关键词： constrained multi-objective optimization Co-evolution Gaining-sharing Knowledge mechanism

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Application of constrained multi-objective Evolutionary Algorithm in multi-Source Compressed-air Pipeline optimization Problems

Application of Constrained Multi-objective Evolutionary Algo...

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10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Yang, Yongkuan Liu, Jianchang Tan, Shubin Wang, Honghai Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

To meet the request of manufacture, several compressor stations usually run at the same time. Decreasing the output pressure of compressor station is one of the major methods to reduce the power utilized by the motors of the compressors. Due to the interaction of several compressor stations with each other, how to set the output pressure of each compressor station becomes a big problem. This paper proposes the constrained multi-objective optimization of multi-Source Compressed-air Pipeline optimization Problems (CMO-MSCPOPs) in compressed-air transmission networks of process industries. The problem formulation involves the minimization of the output pressure of each compressor station. Constraints associated with compressed-air flow rate and compressor stations guarantee the work of each downstream process. In case studies, the model is divided into two topology forms. The optimization of the model is performed using NSGA-II. The solution obtained is a set of Pareto solutions from which a decision making process is highlighted to select a specific preferred solution. Aiming to illustrate the performance of the proposed approach, the tool is applied to two typical network examples considering two compressor stations. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： constrained multi-objective optimization multi-source compressed-air supply NSGA-II multi-attribute decision-making constrained-dominate relationship

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constrained multi-objective Particle Swarm optimization Algorithm

Constrained Multi-objective Particle Swarm Optimization Algo...

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8th International Conference on Intelligent Computing (ICIC)

作者： Gao, Yue-lin Qu, Min N Ethn Univ Inst Informat & Syst Sci Yinchuan Ningxia Peoples R China

ISBN: (纸本)9783642318368

A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.

关键词： particle swarm optimization algorithm constrained multi-objective optimization Pareto neighborhood crossover operation penalty function method

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constrained multi-objective Aerodynamic Shape optimization via Swarm Intelligence 14

Constrained Multi-Objective Aerodynamic Shape Optimization v...

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Zapotecas Martinez, Saul Arias-Montano, Alfredo Coello Coello, Carlos A. Shinshu Univ Fac Engn Nagano 3808553 Japan IPN ESIME Dept Ingn Aeroneut Mexico City 07340 DF Mexico CINVESTAV IPN EVOCINV Dept Comp Mexico City 07360 DF Mexico

ISBN: (纸本)9781450326629

In this paper, we present a multi-objective Particle Swarm Optimizer (MOPSO) based on a decomposition approach, which is proposed to solve constrained multi-objective Aerodynamic Shape optimization Problems (CMO-ASOPs). The constraint-handling technique adopted in this approach is based on the well-known epsilon-constraint method. Since the epsilon-constraint method was initially proposed to deal with constrained single-objective optimization problems, we adapted it so that it could be incorporated into a MOPSO. Our main focus is to solve CMO-ASOPs in an efficient and effective manner. The proposed constrained MOPSO guides the search by updating the position of each particle using a set of solutions considered as the global best according to both the decomposition approach and the epsilon-constraint method. Our preliminary results indicate that our proposed approach is able to outperform a state-of-the-art MOEA in several CMO-ASOPs.

关键词： constrained multi-objective optimization multi-objective Swarm optimization Aerodynamic Shape optimization

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Adaptive constrained multi-objective Biogeography-based optimization Based on Two-stage Elite Selection 9

Adaptive Constrained Multi-objective Biogeography-based Opti...

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9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Wang, Jue Li, Bo Cao, Yinghong Dalian Polytech Univ Coll Informat Sci & Engn Dalian Peoples R China

ISBN: (纸本)9781538630228

a new adaptive constrained multi-objective biogeography-based optimization based on two-stage elite selection, ACMBBO, is proposed to solve constrained multi-objective optimization problems. According to the feature on constrained multi-objective and the evolutionary mechanism of BBO, the model of constrained multi-objective optimization which applies to BBO is built. In the model, the habitat suitability index, which combines with the degree of feasible and the Pareto dominance relation between the individuals, is redefined. Moreover, a new mechanism based on two-stage elite selection is set to preserve the elitist of population individuals. Also dynamic migration strategy is designed to improve the ability for exploitation and the utilization of the better individual. Numerical experiments have shown that ACMBBO is competitive with current other constrained multi-objective optimization algorithms on the convergence and the distribution, and is capable of solving the complex CMOPs more effectively and efficiently.

关键词： constrained multi-objective optimization two-stage elite selection biogeography-based optimization dynamic migration strategy

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multi-objective optimization for multimodal multi-objective multi-Point Shortest Path Problem Considering Unforeseeable Road Eventualities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Xu, Zhiwei Zhang, Kai Del Ser, Javier Li, Miqing Xu, Xin He, Juanjuan Wu, Ni Wuhan Univ Sci & Technol Sch Comp Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc andRe Wuhan 430065 Hubei Peoples R China Tecnalia Basque Res & Technol Alliance BRTA Bizkaia 48160 Bilbao Spain Univ Basque Country Dept Commun Engn UPV EHU Bizkaia 48013 Bilbao Spain Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

multi-objective multi-point shortest path planning problems are commonly encountered in real-world applications. Numerous path planning algorithms have been proposed to accommodate different model assumptions. However, most existing algorithms can only identify a subset of the Pareto optimal paths and overlook equivalent Pareto optimal paths. Relying solely on a subset of Pareto optimal solutions is insufficient to effectively respond to unforeseeable road eventualities in the real-world traffic environment. In this paper, multi-objective multi-point shortest path planning problem is modeled as a multimodal multi-objective optimization problem with necessary points constrains. A multimodal multi-objective evolutionary algorithm using constraint dominance principle-based path comparison strategy and path similarity-based multimodal solutions selection strategy is proposed to address this problem. The proposed constraint dominance principle-based path comparison strategy can effectively navigate through large infeasible regions by relaxing necessary point constraints, thereby obtaining a true constrained Pareto front. The proposed path similarity-based multimodal solutions selection strategy can effectively balance the distribution of solutions in the decision space, thereby preserving multiple equivalent optimal solutions. The proposed algorithm is compared with five state-of-the-art path planning algorithms from the benchmark test suite derived from the 2021 IEEE CEC path planning competition, where city maps are adapted from real transportation networks in Chinese cities, in our experiments. The exceptional performance is demonstrated through thirty independent runs, yielding experimental results that showcase the superiority of the proposed algorithm on the test problem set. This superior performance highlights the potential for designing more resilient path planners suitable for scenarios affected by unpredictable road eventualities.

关键词： Path planning optimization Classification algorithms Shortest path problem Pareto optimization Search problems Heuristic algorithms Roads Evolutionary computation Convergence multi-objective shortest path planning constrained multi-objective optimization multimodal multi-objective optimization multi-objective evolutionary algorithm

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A dual dynamic constraint boundary based constrained multi-objective evolutionary algorithm for small feasible regions

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Zhu, Cong Yang, Yongkuan Kong, Xiangsong Yang, Yanxiang Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & In Xiamen 361024 Peoples R China

Addressing constrained multi-objective optimization problems (CMOPs) with small feasible regions presents a significant challenge, as existing algorithms often struggle to balance feasibility, diversity, and convergence within the population. To overcome this challenge, we propose a dual dynamic constraint boundary-based constrained multi-objective evolutionary algorithm, referred to as TPDCB. In TPDCB, the original CMOP is transformed into two dynamic CMOPs using a dual dynamic constraint boundary strategy to better identify feasible solutions. Specifically, for the two dynamic CMOPs within the constraint relaxation boundary, the first dynamic CMOP primarily focuses on multi-objective optimization, while the second dynamic CMOP equally emphasizes both multi-objective optimization and constraint satisfaction to enhance individual diversity. Furthermore, an auxiliary problem without constraints is introduced by treating constraint violations as an additional optimization objective, which improves the algorithm's global convergence. Finally, a tri-population co-evolution framework is proposed to simultaneously tackle all three constructed problems. The algorithm's performance is evaluated on 22 benchmark problems and three real-world applications, and compared to seven state-of-the-art algorithms. Experimental results demonstrate that TPDCB is competitive in solving CMOPs with small feasible regions.

关键词： constrained multi-objective optimization multi-objective evolutionary algorithm Small feasible region Dynamic constraint boundary Constraint handling Co-evolution

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A decomposition-based dynamic constrained multi-objective task assignment for heterogeneous crowdsensing

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Ji, Jianjiao Guo, Yinan Wang, Wentao Yang, Xiao Gong, Dunwei China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Peoples R China China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 110083 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

Efficient task allocation is a crucial issue of Mobile crowdsensing (MCS). Generally, only homogeneous mobile users like human are selected as the participants, causing a difficulty to meet the spatiotemporal coverage demand on human-unreachable regions. To overcome this drawback, unmanned aerial vehicles are introduced to form heterogeneous MCS, which can be formulated into a dynamic constrained multi-objective task allocation model. Taking the maximum average sensing quality of all tasks and the maximum average remaining budget for each subtask as the optimization objectives, an improved decomposition-based multi- objective evolutionary algorithm is presented to find the optimal allocation scheme. Specifically, the problem is first decomposed into a set of dynamic constrained scalar subproblems. For each subproblem, a stochastic configuration network (SCN)-based initialization is developed to produce the promising population, in which SCNs learn the probabilities of mobile users being allocated to each task. Following that, a reinforcement learning-based autonomous evolutionary strategy is adopted to recommend the most appropriate solvers in terms of the state of current population. A hybrid population update mechanism is then employed to form the high-quality offspring, with the purpose of balancing the feasibility, convergence and diversity. The extensive experiments on 20 dynamic instances are conducted to demonstrate the effectiveness of proposed algorithm compared to other task allocation algorithms.

关键词： Heterogeneous mobile crowdsensing Task assignment Dynamic optimization constrained multi-objective optimization

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constrained multi-objective state transition algorithm via adaptive bidirectional coevolution

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Sun, Yan Zhou, Xiaojun Yang, Chunhua Huang, Tingwen Cent South Univ Sch Automat Changsha 410083 Peoples R China Texas A&M Univ Qatar Doha Qatar

constrained multi-objective optimization problems (CMOPs) involve optimizing multiple conflicting objectives subject to at least one constraint. These constraints often divide the search space into various infeasible regions and narrow or disconnected feasible regions. Most existing constrained multi-objective evolutionary algorithms struggle with imbalanced exploration between feasible and infeasible regions and exhibit poor search capabilities, resulting in populations becoming trapped in locally optimal feasible or infeasible areas. To overcome this limitation, we propose a novel constrained multi-objective state transition algorithm via adaptive bidirectional coevolution (CMOSTA). This algorithm comprises amain population (MP) and a cooperative population (CP), facilitating balanced exploration of both feasible and infeasible regions. CMOSTA adapts environmental selection strategies based on the proportion and distribution of feasible solutions within the MP , promoting efficient information sharing and avoiding unnecessary searches. Additionally, a dynamic Econstraint relaxation strategy is put forward for the MP to prevent stagnation in locally feasible areas. A mating selection approach combining binary tournament and dynamic E-constrained dominance is developed, followed by state transformation operators to generate candidate solutions with both global and local search capabilities. The effectiveness of CMOSTA is verified through 62 benchmark tests and an industrial case study on optimal copper removal, showing superior performance compared to ten well-established constrained multi-objective evolutionary algorithms.

关键词： constrained multi-objective optimization State transition algorithm Adaptive bidirectional coevolution Dynamic E-constraint boundary

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A novel multi-task algorithm for operational optimization of coal mine integrated energy system under multiple uncertainties

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第2期12卷 1-13页

作者： Fei, Xiaotian Ma, Jun Zhang, Jianhua Zhang, Yong Gong, Dunwei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China Xuzhou Univ Technol Sch Elect & Control Engn Xuzhou 221018 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China

The operational optimization of the coal mine integrated energy system (CMIES) is crucial for reducing costs and carbon emissions. However, the system's multi-objective nature, stringent constraints, and the uncertainty of renewable and mine-derived energy make solving its optimization challenging. Thus, this paper first presents a data-driven uncertainty transformation method to address the uncertainty of renewable energy and mining derived energy output;then, a multi-task multi-objective evolutionary algorithm based on adaptive auxiliary tasks (MMOEA-AS) is proposed, which includes a main task and three auxiliary tasks. Meanwhile, an adaptive update strategy for auxiliary tasks and a matching degree-guided knowledge transfer mechanism are proposed to improve the performance of the algorithm. Finally, taking the energy scheduling problem of a coal mine in Shanxi, China as an example, MMOEA-AS is compared with five advanced evolutionary algorithms. The results show that MMOEA-AS can effectively solve the operation optimization of the CMIES, and obtain the optimal scheduling results.

关键词： evolutionary algorithms coal mine integrated energy system multi-task optimization constrained multi-objective optimization

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