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Design and optimization of diffraction-limited storage ring lattices based on many-objective evolutionary algorithms

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Nuclear Science and Techniques 2023年第10期34卷 20-35页

作者： He-Xing Yin Jia-Bao Guan Shun-Qiang Tian Ji-Ke Wang The Institute for Advanced Studies Wuhan UniversityWuhan 430072China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China

Multi-objective evolutionary algorithms(MOEAs) are typically used to optimize two or three objectives in the accelerator field and perform well. However, the performance of these algorithms may severely deteriorate when the optimization objectives for an accelerator are equal to or greater than four. Recently, many-objective evolutionary algorithms(MaOEAs)that can solve problems with four or more optimization objectives have received extensive attention. In this study, two diffraction-limited storage ring(DLSR) lattices of the Extremely Brilliant Source(ESRF-EBS) type with different energies were designed and optimized using three MaOEAs and a widely used MOEA. The initial population was found to have a significant impact on the performance of the algorithms and was carefully studied. The performances of the four algorithms were compared, and the results demonstrated that the grid-based evolutionary algorithm(GrEA) had the best *** OEAs were applied in many-objective optimization of DLSR lattices for the first time, and lattices with natural emittances of 116 and 23 pm·rad were obtained at energies of 2 and 6 GeV, respectively, both with reasonable dynamic aperture and local momentum aperture(LMA). This work provides a valuable reference for future many-objective optimization of DLSRs.

关键词： Storage ring lattices many-objective evolutionary algorithms GrEA algorithm NSGA

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A comparative study of many-objective evolutionary algorithms for the discovery of software architectures

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EMPIRICAL SOFTWARE ENGINEERING 2016年第6期21卷 2546-2600页

作者： Ramirez, Aurora Raul Romero, Jose Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal Cordoba 14071 Spain

During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure such complex systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Along with the expected functionality, non-functional requirements are key at this stage to guide the many design alternatives to be evaluated by software architects. The appearance of Search Based Software Engineering (SBSE) brings an approach that supports the software engineer along the design process. evolutionary algorithms can be applied to deal with the abstract and highly combinatorial optimisation problem of architecture discovery from a multiple objective perspective. The definition and resolution of many-objective optimisation problems is currently becoming an emerging challenge in SBSE, where the application of sophisticated techniques within the evolutionary computation field needs to be considered. In this paper, diverse non-functional requirements are selected to guide the evolutionary search, leading to the definition of several optimisation problems with up to 9 metrics concerning the architectural maintainability. An empirical study of the behaviour of 8 multi- and many-objective evolutionary algorithms is presented, where the quality and type of the returned solutions are analysed and discussed from the perspective of both the evolutionary performance and those aspects of interest to the expert. Results show how some many-objective evolutionary algorithms provide useful mechanisms to effectively explore design alternatives on highly dimensional objective spaces.

关键词： Software architecture discovery Search based software engineering many-objective evolutionary algorithms Multi-objective evolutionary algorithms

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Comparison of many-objective evolutionary algorithms using performance metrics ensemble

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ADVANCES IN ENGINEERING SOFTWARE 2014年 76卷 1-8页

作者： He, Z. Yen, G. G. Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

In this study, we have thoroughly researched on performance of six state-of-the-art Multiobjective evolutionary algorithms (MOEAs) under a number of carefully crafted many-objective optimization benchmark problems. Each MOEA apply different method to handle the difficulty of increasing objectives. Performance metrics ensemble exploits a number of performance metrics using double elimination tournament selection and provides a comprehensive measure revealing insights pertaining to specific problem characteristics that each MOEA could perform the best. Experimental results give detailed information for performance of each MOEA to solve many-objective optimization problems. More importantly, it shows that this performance depends on two distinct aspects: the ability of MOEA to address the specific characteristics of the problem and the ability of MOEA to handle high-dimensional objective space. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Performance metrics ensemble many-objective optimization problem evolutionary algorithms Double elimination tournament selection many-objective evolutionary algorithms Performance metrics

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A modified two-archive evolutionary algorithm for many-objective ecological cascade reservoir operation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2024年第4期24卷 223页

作者： Wei, Zichen Wang, Hui Liao, Futao Wang, Shuai Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China

many-objective optimisation problems (MaOPs) widely exist in real-world applications. Though two-archive2 evolutionary algorithm (Two Arch2) showed good performance in solving MaOPs, its performance highly depends on the update methods of convergence archive (CA) and diversity archive (DA). To further improve the efficiency of updating two archives, this paper proposes a modified two-archive evolutionary algorithm (called MTaEA). Firstly, MTaEA adopts two different strategies to update CA. Then, a new update strategy based on radial projection and parallel distance is designed for DA. To validate the performance of MTaEA, two benchmark sets (DTLZ and MaF) with 3, 5, 10, 15, and 20 objectives are tested. Results show MTaEA obtains competitive performance when compared with six other state-of-the-art approaches. Finally, the proposed MTaEA is applied to many-objective ecological cascade reservoir operation in central China. Simulation results indicate MTaEA still achieves promising performance.

关键词： many-objective evolutionary algorithms many-objective optimisation problems parallel distance two-archive cascade reservoir operation scheduling

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The charging station and swapping station site selection with many-objective evolutionary algorithm

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APPLIED INTELLIGENCE 2023年第14期53卷 18041-18060页

作者： He, Yongqiang Zhang, Yanjun Fan, Tian Cai, Xingjuan Xu, Yubin Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China

The battery swap mode is a novel way of energy supplement for electric vehicles. Inevitably, there are some business transactions between battery swapping station (BSS) and battery centralized charging station (BCCS) in the mode. Therefore, it is essential to plan the construction of BSS and BCCS uniformly. Moreover, the needs of enterprises and users are not taken into account simultaneously in the existing site selection model. To resolve this problem, a many-objective joint site selection (MOJSS) model of BSS and BCCS is proposed in this paper. It mainly includes four objective functions: construction cost, coverage rate, investment income and satisfaction, which consider distance constraint between user demand points and the BSS, distance constraint between BBS and BSS, and the service ability constraint of BSS and the BCCS. To better solve the proposed model, a Grid-based evolutionary algorithm based on hybrid environment selection strategy is proposed. Furthermore, the segmented integer coding strategy and the specific genetic operation are designed based on the characteristic of model. It is compared with the existing many-objective evolutionary algorithms on standard test problems. Then the algorithm is applied to solve the established model. The experimental result demonstrated the reasonableness and effectiveness of proposed model. Finally, the site selection results are illustrated by a set of solutions.

关键词： Electric vehicles many-objective joint site selection BCCS BSS many-objective evolutionary algorithms

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A two-stage many-objective evolutionary algorithm with dynamic generalized Pareto dominance

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第11期37卷 9833-9862页

作者： Wang, Hui Wei, Zichen Yu, Gan Wang, Shuai Wu, Jiali Liu, Jiawen Nanchang Inst Technol Sch Informat Engn Nanchang Jiangxi Peoples R China Fuyang Normal Univ Sch Informat Engn Fuyang Peoples R China

many-objective evolutionary algorithms (MaOEAs) are widely used to solve many-objective optimization problems. As the number of objectives increases, it is difficult to achieve a balance between the population diversity and the convergence. Additionally, the selection pressure decreases rapidly. To tackle these issues, this paper proposes a two-stage many-objective evolutionary algorithm with dynamic generalized Pareto dominance (called TS-DGPD). First, a two-stage method is utilized for environmental selection. The first stage employs the cosine distance to accelerate the convergence. The second stage uses L p ${L}_{p}$-norm maintain the population diversity. Moreover, a dynamic generalized Pareto dominance (DGPD) is used to increase the selection pressure of the population. To evaluate the performance of TS-DGPD, we compare it with several other MaOEAs on two benchmark sets with 3, 5, 8, 10, 15, and 20 objectives. Experimental results show that TS-DGPO performs satisfactorily on convergence and diversity.

关键词： generalized Pareto dominance many-objective evolutionary algorithms many-objective optimization problems multiobjective optimization two-stage selection

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A multistage evolutionary algorithm for many-objective optimization

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INFORMATION SCIENCES 2022年 589卷 531-549页

作者： Shen, Jiangtao Wang, Peng Dong, Huachao Li, Jinglu Wang, Wenxin Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Convergence and diversity are of high significance to many-objective optimization, which are considered by most state-of-the-art many-objective evolutionary algorithms (MaOEAs) simultaneously. However, it is not easy to balance them during the optimization process due to their conflicting nature. This study proposes a multistage MaOEA to address this issue, where convergence and diversity are processed respectively at different optimization stages. At the first stage, the population approaches Pareto front rapidly and the diversity is ignored. After the population is converged, the diversity will be emphasized by applying the decision variable clustering method at the second stage. When the population achieves high convergence and diversity, the algorithm will enter the last stage, where the quality of the solution set is fine-tuned by substituting those solutions with worse convergence and diversity degrees. As demonstrated by the experimental results with peer competitors on common benchmark problems, that the proposed algorithm is promising. (C) 2022 Elsevier Inc. All rights reserved.

关键词： many-objective evolutionary algorithms many-objective optimization problems Multistage optimization Convergence and diversity

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many-objective evolutionary algorithm based on parallel distance for handling irregular Pareto fronts

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SWARM AND evolutionary COMPUTATION 2024年 86卷

作者： Wei, Zichen Wang, Hui Wang, Shuai Zhang, Zhixia Cui, Zhihua Wang, Feng Peng, Hu Zhao, Jia Nanchang Inst Technol Sch Informat Engn Nanchang Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China

In recent years, various many -objective evolutionary algorithms (MaOEAs) have been proved to be successful in solving many -objective optimization problems (MaOPs). However, the performance of most MaOEAs is seriously affected when handling MaOPs with irregular Pareto fronts (PFs). In this paper, a new MaOEA variant based on parallel distance (called PDMaOEA) is proposed to solve MaOPs with irregular PFs. Firstly, two new metrics based on parallel distance are designed. The first one termed diversity metric can adapt to irregular PFs. The second one called comprehensive selection metric can consider both diversity and convergence simultaneously. Based on the two metrics, a mating selection method and an environmental selection strategy are proposed. In the mating selection, solutions with good convergence or diversity are chosen to improve the quality of offspring population. In the environmental selection, the selection pressure is significantly enhanced by the two metrics. Experimental study is validated on 19 irregular problems with different shapes of PFs. Performance of the proposed PDMaOEA is compared with six state-of-the-art algorithms. Statistical analysis shows that the proposed approach is competitive in handling MaOPs with irregular PFs.

关键词： many-objective evolutionary algorithms (MaOEAs) many-objective optimization problems (MaOPs) Optimization Irregular Pareto front Parallel distance

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A hypervolume fraction-based adaptive evolutionary algorithm for many-objective optimization and the application to electromagnetic device design

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 137卷

作者： Lin, Jie Zhang, Sheng Xin Xu, Yi Jiao Zheng, Shao Yong Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China

Performance of many-objective evolutionary algorithms (MaOEAs) heavily depends on the environmental selection strategy which determines the offspring for next generations. One kind of selection strategy may only suit certain kinds of optimization problems. Moreover, one single strategy might not always work well at different evolutionary stages. To adaptively adjust the environmental selection strategy, this paper proposes a hyper- volume fraction-based adaptive evolutionary algorithm (HFAEA). First, a hypervolume fraction-based estimation method is proposed to address the difficulty in detecting the feature of Pareto front. It calculates the ratio of the hypervolume of population coverage to the hypervolume of coordinate axis coverage. With a small or large hypervolume fraction, Pareto front is regarded as irregular or regular respectively and an adaptive switching strategy adaptively selects a proposed vector angle-based strategy or an improved reference vector-based strategy. HFAEA is compared with five state-of-the-art algorithms on 24 problems with a large hypervolume fraction and 24 problems with a small hypervolume fraction. Experimental results show that HFAEA is the most competitive in handling different kinds of problems. It outperforms algorithms that designed for irregular problems as well as algorithms that use uniformly distributed reference vectors in irregular problems. These findings highlight the effectiveness of the proposed hypervolume fraction-based estimation method. The superior performance is also demonstrated in two electromagnetic device optimization problems, including the designs of a compact single-layer butler matrix and a broadband filtering power divider, where better results than original ones are achieved and HFAEA also outperforms state-of-the-art MaOEAs.

关键词： many-objective evolutionary algorithms Adaptive environmental selection Hypervolume fraction Electromagnetic device optimization

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ESOEA: Ensemble of single objective evolutionary algorithms for many-objective optimization

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SWARM AND evolutionary COMPUTATION 2019年 50卷 100511-000页

作者： Pal, Monalisa Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit 203 Barrackpore Trunk Rd Kolkata 700108 India

Inspired by the success of decomposition based evolutionary algorithms and the necessary search for a versatile many-objective optimization algorithm which is adaptive to several kinds of characteristics of the search space, the proposed work presents an adaptive framework which addresses many-objective optimization problems by using an ensemble of single objective evolutionary algorithms (ESOEA). It adopts a reference-direction based approach to decompose the population, followed by scalarization to transform the many-objective problem into several single objective sub-problems which further enhances the selection pressure. Additionally, with a feedback strategy, ESOEA explores the directions along difficult regions and thus, improving the search capabilities along those directions. For experimental validation, ESOEA is integrated with an adaptive Differential Evolution and experimented on several benchmark problems from the DTLZ, WFG, IMB and CEC 2009 competition test suites. To assess the efficacy of ESOEA, the performance is noted in terms of convergence metric, inverted generational distance, and hypervolume indicator, and is compared with numerous other multi- and/or many-objective evolutionary algorithms. For a few test cases, the resulting Pareto-fronts are also visualized which help in the further analysis of the results and in establishing the robustness of ESOEA.

关键词： many-objective evolutionary algorithms Differential evolution Pareto-optimality Non-dominated solutions DTLZ, WFG, IMB and CEC 2009 competition test problems

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