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A dynamic tri-population multi-objective evolutionary algorithm for constrained multi-objective optimization problems

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EVOLUTIONARY INTELLIGENCE 2024年第4期17卷 2791-2806页

作者： Yang, Yongkuan Yan, Bing Kong, Xiangsong 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

Balancing between the convergence, feasibility, and diversity of the population is the key to solving constrained multi-objective optimization problems (CMOPs). However, the existing constrained multi-objective optimization evolutionary algorithm (CMOEAs) face challenges in converging to the constrained pareto front (CPF) with well-distributed feasible solutions. To address this issue, this paper proposes a dynamic tri-population multi-objective evolutionary algorithm, called TDPSCMO. In the initial phase, the first and second populations evolve dynamically to solve the original CMOP and the unconstrained multi-objective optimization Problem (MOP). This dynamic offspring generation emphasizes convergence and feasibility. In the later stages, the allocation of computing resources to the first population is adjusted based on the variation in offspring numbers. Simultaneously, a third population is introduced to enhance population diversity by treating Constraint Violation degrees as an additional objective function, thus capturing more valuable information. The performance of TDPSCMO is further tested through 57 benchmark test problems and 21 real-world applications using several state-of-the-art algorithms. The results show the competitiveness of the proposed algorithm when addressing CMOPs.

关键词： Evolutionary algorithm constrained multi-objective optimization Dynamic tri-population Additional objective function

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An Archive-assisted Two-stage and Dual-population Evolutionary Algorithm for constrained multi-objective optimization Problems 43

An Archive-assisted Two-stage and Dual-population Evolutiona...

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43rd Chinese Control Conference (CCC)

作者： Zhang, Wenjuan Ma, Huan Zhang, Wei Liu, Yuanchao Tan, Shubin Liu, Jianchang Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligent Syst Optim Shenyang 110819 Peoples R China Beiying Iron & Steel Grp Co Ltd Benxi 117017 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

The key to solving constrained multiobjective optimization problems (CMOPs) is to balance objective optimization and constraint satisfaction. But the existing evolutionary algorithms suffer from great difficulties in striking a balance between them, especially in dealing with CMOPs with complex constraints. To solve the issue, an archive-assisted two-stage and dual-population evolutionary algorithm, referred as ATD-CMOEA, is proposed. The aim of Stage 1 is to explore the entire objective space and discover the promising regions, which is achieved by two populations with different priorities of constraints and objectives. Meanwhile, feasible solutions discovered during the evolutionary process are archived in normal archive. The purpose of Stage 2 is to get a series of well-distributed Pareto optimal solutions by the simultaneous evolution of the main population, the assistant population and normal archive. Experimental results on two test suites demonstrate the competitiveness of the proposed ATD-CMOEA when compared with other seven state-of-art algorithms.

关键词： constrained multi-objective optimization Evolutionary algorithm Two-stage framework Dual-population

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Three-Role-Community Evolutionary Algorithm for constrained multi-objective optimization Problems 20th

Three-Role-Community Evolutionary Algorithm for Constrained ...

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20th International Conference on Intelligent Computing (ICIC)

作者： Wang, Denghui Guo, Jinglei Deng, Yameng Cent China Normal Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9789819755776;9789819755783

Inspired by the concept of divide-and-conquer, existing multi-task/ multi-population constraint evolutionary algorithms (CMOEAs) have often employed an auxiliary population that disregards all constraints in order to simplify the problem. However, when dealing with complex Constraint Pareto Fronts (CPF), many existing approaches encounter difficulties in maintaining diversity and avoiding local optima. To address the above issue, the Three-role-community based CMOEA (TRC) which focuses on roles within the population is introduced to eliminate the burden of knowledge transfer between multi-task or multi-population CMOEAs. TRC establishes three essential roles: the feasible group, tasked with identifying CPFs;the exploration group, dedicated to discovering the unconstrained Pareto Front (UPF);and the diversity group, responsible for preserving population diversity. By dynamically adjusting the allocation of individuals to these roles, TRC effectively navigates the evolving problem landscape. Moreover, a flexible and straightforward quota allocation strategy for offspring size is designed in TRC. Rigorously tested on MW and DASCMOP test suites, TRC's performance is either better than or at least comparable to some state-of-the-art algorithms.

关键词： Evolutionary algorithm constrained multi-objective optimization community evolutionary algorithm multitask optimization

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Impact of Estimation Method of Ideal/Nadir Points on Practically-constrained multi-objective optimization Problems for Decomposition-Based multi-objective Evolutionary Algorithm

Impact of Estimation Method of Ideal/Nadir Points on Practic...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Fukumoto, Hiroaki Oyama, Akira Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Sagamihara Kanagawa Japan

ISBN: (纸本)9781728124858

To apply MOEAs to industrial problems, the estimation of the ideal and nadir points becomes crucial in order to handle the differently scaled objectives by normalization and to handle the unknown Pareto Front. In this paper, the impact of the estimation methods of the ideal and nadir points for decomposition-based multi-objective evolutionary algorithms is examined on some constrained optimization problems whose constraints are explicitly designed to have the characteristics of the practical problems. The numerical experiments show that the estimation method has great impact on the performance of MOEAs.

关键词： constrained multi-objective optimization decomposition estimation of ideal and nadir points

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Handling constrained multi-objective optimization with objective Space Mapping to Decision Space Based on Extreme Learning Machine

Handling Constrained Multi-Objective Optimization with Objec...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Zhang, Hao Tao, Ku Ma, Lianbo Yong, Yibo Chinese Acad Sci Key Lab Networked Control Syst Beijing Peoples R China Chinese Acad Sci Shenyang Inst Automat Beijing Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Beijing Peoples R China Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9781728169293

constrained multi-objective optimization is frequently encountered from the point of view of practical problem solving. The difficulty of constrained multi-objective optimization is how to offer guarantee of finding feasible optimal solutions within a specified number of iterations. To address the issue, this paper proposes an innovative optimization framework with objective space mapping to decision space for constrained multi objective optimization and a novel multi-objective optimization algorithms are proposed based on this framework. Extreme learning machine implements prediction of decision variables from modified objective values with distance measure and adaptive penalty. This algorithm employs the framework of artificial bee colony to divide this optimization process into two phases: the employed bees and the onlooker bees. In the phase of employed bees, multi-objective strategy employs fast non-dominant sort and crowded distance to push the population toward Pareto front. In the phase of onlooker bees, multi-objective strategy employs Tchebycheff approach to enhance the population diversity. The experimental results on a series of benchmark problems suggest that our proposed algorithm is quite effective, in comparison to other state-of-the-art constrained multi-objective optimizers.

关键词： constrained multi-objective optimization extreme learning machine artificial bee colony decomposition non-domination

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A New constrained multi-objective optimization Problems Algorithm Based on Group-sorting 19

A New Constrained Multi-objective Optimization Problems Algo...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Liu, Yijun Li, Xin Hao, Qijia Xidian Univ Xian Peoples R China

ISBN: (纸本)9781450367486

constrained multi-objective optimization problems(CMOPs) have wide applications in many areas. One of the difficulties in solving CMOPs is to handle constraints and optimize objective values simultaneously. In this paper, three improvements are proposed for CMOPs. Firstly, a new strategy of adaptive grouping is proposed to ensure the diversity of the algorithm. Secondly, each sub-population evolves according to the designed crossover operator, which improves the searchability of the algorithm, thus accelerates the convergence process of the algorithm. Finally, a new sorting method based on the information of representative solutions is designed to measure the quality of individuals. The experimental results show that the proposed algorithm performs better in convergence and diversity.

关键词： constrained multi-objective optimization adaptive grouping the mixed crossover operator

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A constrained multi-objective optimization for the Evaluation and Selection of Monitored Bridges in Road Network 8

A Constrained Multi-objective Optimization for the Evaluatio...

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8th International Conference on Hydraulic and Civil Engineering - Deep Space Intelligent Development and Utilization Forum (ICHCE)

作者： Xu, Fangyuan Chao, Sugong Chen, Yuanli Wang, Jiaying Key Lab Detect & Maintenance Technol Rd & Bridge Hangzhou Peoples R China Zhejiang Inst Commun Hangzhou Peoples R China

ISBN: (纸本)9781665465533

The selection of bridges to be monitored in an extensive road network requires comprehensive considerations such as total monitoring cost, bridge types and span, traffic flow, regional economic radiation and bridge service performance, and might control the total quantity of monitoring bridges within a certain proportion. Based on evolutionary algorithm NSGA2, this paper improved the mutation algorithm with respect to the binary integer (0,1) problem and adopted Tabu list to reduce the redundancy of iterative search. It proposed a constrained multi-objective optimization by introducing sparse random matrix and constraint violation indicator to restrain the proportion. Taking the selection of the monitored ones from total 207 bridges in a county road network as an example, the proposed method were used to analyze the distribution of Pareto solution set, the satisfaction of constraints and the calculation efficiency under different corresponding evolutionary simulation parameters. The result shows that the evaluation based on the crowding distance in the traditional NSGA2 cannot select the optimal bridges to be monitored effectively. By introducing a fuzzy membership degree to obtain the multi-objective decision weights, the extreme value of decision function can be used for selecting the final decision plan of monitored bridges in road network.

关键词： selection of monitored bridges NSGA2 constrained multi-objective optimization Tabu search fuzzy membership degree

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AN INTEGRATED APPROACH TO DESIGNING ROBUST TURBOCOMPRESSORS ON GAS BEARINGS THROUGH SURROGATE MODELING AND constrained multi-objective optimization

AN INTEGRATED APPROACH TO DESIGNING ROBUST TURBOCOMPRESSORS ...

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ASME International Design Engineering Technical Conferences / Computers and Information in Engineering Conference (IDETC-CIE) / 49th Design Automation Conference (DAC)

作者： Massoudi, Soheyl Picard, Cyril Schiffmann, Jurg Ecole Polytech Fed Lausanne Lab Appl Mech Design CH-1015 Lausanne Switzerland MIT Dept Mech Engn Cambridge MA 02139 USA

ISBN: (纸本)9780791887318

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocompressors supported on gas bearings, which utilizes surrogate models and ensemble learning with artificial neural networks. The proposed approach addresses the limitations of nominal and separate optimizations by integrating all relevant design aspects into a single optimization problem. A multi-objective optimization is carried out, considering four objectives and over twenty constraints, including robustness against manufacturing deviations of the radial and axial bearings in terms of stability, load capacity, and efficiency, as well as robustness against performance metric gradients. The proposed methodology maximizes the compressor's range in speeds and mass flow, while also maximizing the signal-to-noise ratio of the isentropic efficiency over the compressor map. Additionally, the approach maximizes system efficiency, taking into account component losses and isentropic efficiency of the compressor. To enable rapid and automated integrated design, the methodology reduces the compressor representation to a fully cylindrical representation. The study finds that the proposed methodology has the potential to significantly enhance the overall performance of turbocompressors in terms of efficiency, stability, and robustness. The methodology eliminates the need for sequential and iterative design steps, providing an optimal starting point for higher representation of the system with CFD and finite elements study. Furthermore, the proposed methodology has broad applications, including the optimization of other complex and interdependent systems in various fields. This study highlights the crucial role of a comprehensive and integrated approach to turbocompressor design and provides a valuable framework for future researc

关键词： Herringbone grooved journal bearings gas bearings micro-turbomachinery integrated design robust design constrained multi-objective optimization artificial neural networks

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Using Generative Adversarial Networks for Efficient constrained multi-objective optimization 24

Using Generative Adversarial Networks for Efficient Constrai...

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3rd International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Chao, Wang Jing, Zhang Zheng, Zhabang Liang, Zhushou Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Anhui Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Anhui Peoples R China

ISBN: (纸本)9798400709777

To address constrained multi-objective optimization problems through evolutionary algorithms, both the constraint handling technique and the search operator are crucial. However, existing approaches often prioritize the former for identifying promising solutions, while the critical role of the latter in producing feasible solutions is frequently overlooked. This paper shifts the focus to the search operator's contribution by introducing an offspring generation technique that is guided by a model-driven approach leveraging adversarial learning. The method involves dividing the population into groups of feasible and infeasible solutions, which are then used as adversarial examples during the model's training phase. If the solution crafted by the generator is impractical, the feasible subset guides the evolution towards the feasible solution space, while the infeasible subset helps define the boundaries of the feasible space. This adversarial training technique progressively replicates the distribution of feasible regions within the decision space. By utilizing adversarial learning, this innovative method generates offspring with significant potential, thereby accelerating the convergence of the population towards the optimal feasible region.

关键词： constrained multi-objective optimization Generation adversarial Network Offspring generation Evolutionary algorithm

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Optimal Scheduling of Microgrid Using constrained multi-objective optimization with Additional objectives 6

Optimal Scheduling of Microgrid Using Constrained Multi-Obje...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Hua, Yukai Liao, Binrong Zhang, Hao Liu, Liming Li, Yuxuan Yang, Yongkuan Fujian Dongqiao Thermal Power Co Ltd Quanzhou 362000 Peoples R China Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China

ISBN: (纸本)9798350377859;9798350377842

To address the issues in the constrained multi-objective optimal scheduling problem of microgrids, such as encountering infeasible solutions and a low proportion of feasible solutions, we propose a constrained multi-objective optimization algorithm assisted by an additional objective function. Three swarms in a push-and-pull co-evolutionary framework are constructed: the first swarm optimizes the original constrained problem, the second swarm optimizes constraint violation as an additional objective to effectively increase the proportion of feasible solutions, and the third swarm uses push-pull search and improved constraint relaxation techniques to transform high-quality infeasible solutions into feasible ones during the search process. This helps the populations overcome infeasibility barriers and escape local optima. All feasible solutions found by the three populations during the search process are stored in a separate archive to ensure the feasibility and diversity distribution of the solutions. The proposed constrained multi-objective optimization algorithm is applied to optimize the scheduling of microgrid equipment by establishing a microgrid optimization model for combined cooling, heating, and power supply, with system operating cost and environmental management cost as the optimization objectives, and considering the constraints in actual operation. The results demonstrate that the proposed algorithm can reasonably adjust the output of each distributed power source and improve the flexibility and economy of microgrid optimization.

关键词： Microgrid optimal scheduling constrained multi-objective optimization constrained multi-objective optimization algorithm

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