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2nd Asia Conference on Algorithms, Computing and Machine Learning (CACML)

作者： Wang, Mingjing Chen, Long Chen, Huiling Southeast Univ Nanjing Jiangsu Peoples R China Wenzhou Univ Wenzhou Peoples R China

ISBN: (纸本)9781450399449

many-objective optimization problems (MaOPs), are the most difficult problems to solve when it comes to multiobjective optimization issues (MOPs). MaOPs provide formidable challenges to current multiobjective evolutionary methods such as selection operators, computational cost, visualization of the high-dimensional trade-off front. Removal of the reductant objectives from the original objective set, known as objective reduction, is one of the most significant approaches for MaOPs, which can tackle optimization problems with more than 15 objectives is made feasible by its ability to greatly overcome the challenges of existing multi-objective evolutionary computing techniques. In this study, an objective reduction evolutionary multiobjective algorithm using adaptive density-based clustering is presented for MaOPs. The parameters in the density-based clustering can be adaptively determined by depending on the data samples constructed. Based on the clustering result, the algorithm employs an adaptive strategy for objective aggregation that preserves the structure of the original Pareto front as much as feasible. Finally, the performance of the proposed multiobjective algorithms on benchmarks is thoroughly investigated. The numerical findings and comparisons demonstrate the efficacy and superiority of the suggested multiobjective algorithms and it may be treated as a potential tool for MaOPs.

关键词： objective Reduction Evolutionary Multiobjective Algorithm Density-Based Clustering many-objective optimization problem

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A hybrid many-objective optimization algorithm for coal green production problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第6期33卷 e6040-e6040页

作者： Cui, Zhihua Zhang, Jiangjiang Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

The problem of convergence and diversity in the course of population evolution is difficult to be balanced for solving the many-objective optimization problem (MaOP). To track with the problem, a many-objective optimization algorithm is designed. In the algorithm, a hybrid selection mechanism under the concurrent integration strategy is built to improve algorithm performance by employing the different selection operators. The concurrent integration strategy can select the suitable operator to balance the convergence and diversity of the solution in the course of the population evolutionary. To verify the effectiveness of the algorithm, the designed algorithm is compared with other five excellent many-objective algorithms on DTLZ and WFG test problem. What is more, the designed algorithm is applied to solve the coal green production optimization problem. The simulation results show that the performance of designed algorithm is superior to whether the DTLZ and WFG test problem or the application problem.

关键词： coal green production optimization problem concurrent integration strategy convergence and diversity many-objective optimization problem

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many-objective evolutionary algorithms based on reference-point-selection strategy for application in reactor radiation-shielding design

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NUCLEAR SCIENCE AND TECHNIQUES 2025年第6期36卷 1-15页

作者： Liu, Cheng-Wei Sun, Ai-Kou Lei, Ji-Chong Qu, Hong-Yu Yang, Chao Yu, Tao Chen, Zhen-Ping Univ South China Sch Nucl Sci & Technol Hengyang 421001 Peoples R China Univ South China Key Lab Adv Nucl Energy Design & Safety Minist Educ Hengyang 421000 Peoples R China

In recent years, the development of new types of nuclear reactors, such as transportable, marine, and space reactors, has presented new challenges for the optimization of reactor radiation-shielding design. Shielding structures typically need to be lightweight, miniaturized, and radiation-protected, which is a multi-parameter and multi-objective optimization problem. The conventional multi-objective (two or three objectives) optimization method for radiation-shielding design exhibits limitations for a number of optimization objectives and variable parameters, as well as a deficiency in achieving a global optimal solution, thereby failing to meet the requirements of shielding optimization for newly developed reactors. In this study, genetic and artificial bee-colony algorithms are combined with a reference-point-selection strategy and applied to the many-objective (having four or more objectives) optimal design of reactor radiation shielding. To validate the reliability of the methods, an optimization simulation is conducted on three-dimensional shielding structures and another complicated shielding-optimization problem. The numerical results demonstrate that the proposed algorithms outperform conventional shielding-design methods in terms of optimization performance, and they exhibit their reliability in practical engineering problems. The many-objective optimization algorithms developed in this study are proven to efficiently and consistently search for Pareto-front shielding schemes. Therefore, the algorithms proposed in this study offer novel insights into improving the shielding-design performance and shielding quality of new reactor types.

关键词： many-objective optimization problem Evolutionary algorithm Radiation-shielding design Reference-point-selection strategy

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Multi-objective optimization of Satellite-ground time synchronization Scheduling problem

Multi-objective Optimization of Satellite-ground time synchr...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Wang, Zhaohui Zhang, Zhongshan Chen, Yingwu Natl Univ Def Technol Coll Syst Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781728121536

The task planning of satellite-ground time synchronization (SGTSTP) in global navigation satellite system is a complex many-objective ground station scheduling problem. In this paper, we first provide a mathematical formulation of the over-subscribed problem and compare with traditional scheduling problems likes job-shop scheduling problem (JSP) and satellite range scheduling problems (SRSP). In application of the Beidou Navigation System of China, with the limit of ground resource and visible time between satellites and antennas, it is no doubt a difficult problem to solve, besides, there are several objectives for SGTSTP. To solve this SGTSTP problem with efficiency and effectiveness, we propose a solving method based on decomposition-and-integration (DI), and transform SGTSTP from many-objective optimization problem (MaOPs) into a multi-objective optimization problem (MOP), to make it suitable for a multi-objective evolutionary algorithm (MOEA). Meanwhile, evolutionary many-objective optimization algorithm (EMOA) is used for original objectives as comparison. We embed the DI method into two classes of evolutionary algorithm frameworks. DI-MOEA works on a transformed two-objective version of the SGTSTP while DI-EMOA deals with the original four-objective SGTSTP problem. Computational results on two well-designed instances show that the DI-MOEA achieves worse convergence and diversity but better objective value and computational efficiency compared to the DI-EMOA.

关键词： many-objective optimization problem multi-objective optimization evolutionary algorithm job-shop scheduling ground station scheduling

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Entropy based evolutionary algorithm with adaptive reference points for many-objective optimization problems

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INFORMATION SCIENCES 2018年 465卷 232-247页

作者： Zhou, Chong Dai, Guangming Zhang, Cuijun Li, Xiangping Ma, Ke China Univ Geosci Sch Comp Wuhan 430074 Hubei Peoples R China Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Hubei Peoples R China

many-objective optimization problems (MaOPs) have attracted more and more attention due to its challenges for multi-objective evolutionary algorithms. Reference points or weight vectors based evolutionary algorithms have been developed successfully for solving MaOPs. However, these algorithms do not solve efficiently the MaOPs with irregular Pareto fronts, such as disconnected, degenerate, and inverted. Although some algorithms with adaptive weight vectors or reference points are designed to handle the problems with irregular shapes of Pareto fronts, they still exist some drawbacks. These adaptive algorithms do not obtain good performance in solving regular problem. For solving regular and irregular Pareto fronts of the problems, a novel entropy based evolutionary algorithm with adaptive reference points, named EARPEA, is proposed to solve regular and irregular many-objective optimization problems. Entropy computed based on reference points and a learning period are employed to control adaptation of the reference points. In addition, in order to maintain diversity of the reference points, a reference point adaptation method based on cosine similarity is designed in the adjusting reference point phase. The proposed algorithm is empirically compared with eight state-of-the-art many-objective evolutionary algorithms on 72 instances of 18 benchmark problems. The comparative results demonstrate that the overall performance of the proposed algorithm is superior to the counterparts on MaOPs with regular and irregular Pareto fronts. (C) 2018 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problem Evolutionary algorithm Entropy Adaptive reference points Irregular and regular Pareto front

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A hybridized angle-encouragement-based decomposition approach for many-objective optimization problems

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APPLIED SOFT COMPUTING 2019年 78卷 355-372页

作者： Su, Yuchao Wang, Jia Ma, Lijia Wang, Xiaozhou Lin, Qiuzhen Coello Coello, Carlos A. Chen, Jianyong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China IPN Dept Comp Sci CINVESTAV Mexico City 07360 DF Mexico

Due to the large objective space when handling many-objective optimization problems (MaOPs), it is a challenging work for multi-objective evolutionary algorithms (MOEAs) to balance convergence and diversity during the search process. Although a number of decomposition-based MOEAs have been designed for the above purpose, some difficulties are still encountered for tackling some difficult MaOPs. As inspired by the existing decomposition approaches, a new Hybridized Angle-Encouragement-based (HAE) decomposition approach is proposed in this paper, which is embedded into a general framework of decomposition-based MOEAs, called MOEA/D-HAE. Two classes of decomposition approaches, i.e., the angle-based decomposition and the proposed encouragement-based boundary intersection decomposition, are sequentially used in HAE. The first one selects appropriate solutions for association in the feasible region of each subproblem, which is expected to well maintain the diversity of associated solutions. The second one acts as a supplement for the angle-based one under the case that no solution is located in the feasible region of subproblem, which aims to ensure the convergence and explore the boundaries. By this way, HAE can effectively combine their advantages, which helps to appropriately balance convergence and diversity in evolutionary search. To study the effectiveness of HAE, two series of well-known test MaOPs (WFG and DTLZ ) are used. The experimental results validate the advantages of HAE when compared to other classic decomposition approaches and also confirm the superiority of MOEA/D-HAE over seven recently proposed many-objective evolutionary algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： many-objective optimization problem Angle-based decomposition Encouragement-based decomposition

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Nadir point estimation for many-objective optimization problems based on emphasized critical regions

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SOFT COMPUTING 2017年第9期21卷 2283-2295页

作者： Wang, Handing He, Shan Yao, Xin Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist Educ Xian 710071 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Nadir points play an important role in many-objective optimization problems, which describe the ranges of their Pareto fronts. Using nadir points as references, decision makers may obtain their preference information for many-objective optimization problems. As the number of objectives increases, nadir point estimation becomes a more difficult task. In this paper, we propose a novel nadir point estimation method based on emphasized critical regions for many-objective optimization problems. It maintains the non-dominated solutions near extreme points and critical regions after an individual number assignment to different critical regions. Furthermore, it eliminates similar individuals by a novel self-adaptive -clearing strategy. Our approach has been shown to perform better on many-objective optimization problems (between 10 objectives and 35 objectives) than two other state-of-the-art nadir point estimation approaches.

关键词： Multi-objective evolutionary algorithm Nadir point Critical region many-objective optimization problem Decision making

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Novel PIO Algorithm with Multiple Selection Strategies for many-objective optimization problems

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Complex System Modeling and Simulation 2021年第4期1卷 291-307页

作者： Zhihua Cui Lihong Zhao Youqian Zeng Yeqing Ren Wensheng Zhang Xiao-Zhi Gao School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Computing University of Eastern FinlandKuopioFI-70211Finland

With the increase of problem dimensions,most solutions of existing many-objective optimization algorithms are ***,the selection of individuals and the retention of elite individuals are *** algorithms cannot provide sufficient solution precision and guarantee the diversity and convergence of solution sets when solving practical many-objective industrial ***,this work proposes an improved many-objective pigeon-inspired optimization(ImMAPIO)algorithm with multiple selection strategies to solve many-objective optimization *** selection strategies integrating hypervolume,knee point,and vector angles are utilized to increase selection pressure to the true Pareto ***,the accuracy,convergence,and diversity of solutions are *** is applied to the DTLZ and WFG test functions with four to fifteen objectives and compared against NSGA-III,GrEA,MOEA/D,RVEA,and many-objective Pigeon-inspired optimization *** results indicate the superiority of ImMAPIO on these test functions.

关键词： pigeon-inspired optimization algorithm many-objective optimization problem multiple selection strategy elite individual retention

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Computational Complexity Measures for many-objective optimization problems

Computational Complexity Measures for Many-objective Optimiz...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

Multi-objective optimization problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed "many-objective optimization problems (MaOPs)". While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a Multi-objective Evolutionary Algorithm (MOEA) and its MaOP counterpart. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： multi-objective optimization problem MOP many-objective optimization problem MaOP computational complexity multi-objective evolutionary algorithm MOEA

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Computational Complexity Measures for many-objective optimization problems

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Procedia Computer Science 2014年 36卷 185-191页

作者： David M. Curry Cihan H. Dagli Department of Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla Missouri 65409

Multi-objective optimization problems (MOPs) are commonly encountered in the study and design of complex systems. Pareto dominance is the most common relationship used to compare solutions in MOPs, however as the number of objectives grows beyond three, Pareto dominance alone is no longer satisfactory. These problems are termed “many-objective optimization problems (MaOPs)”. While most MaOP algorithms are modifications of common MOP algorithms, determining the impact on their computational complexity is difficult. This paper defines computational complexity measures for these algorithms and applies these measures to a Multi-objective Evolutionary Algorithm (MOEA) and its MaOP counterpart.

关键词： multi-objective optimization problem MOP many-objective optimization problem MaOP computational complexity multi- objective evolutionary algorithm MOEA

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