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A many-objective evolutionary algorithm with Spatial Division and Angle Culling Strategy

Chinese Journal of Electronics 2021年第3期30卷 437-443页

作者： WANG Hongbo YANG Fan TIAN Kena TU Xuyan School of Computer and Communication Engineering Beijing Key Lab.of Knowledge Engineering for Materials ScienceUniversity of Science and Technology Beijing

In a specific project, how to find a reasonable balance between a plurality of objectives and their optimal solutions has always been an important aspect for researchers. As a trade off between fast convergence and a rich diversity, a many-objective evolutionary algorithm based on a spatial division and angle-culling strategy(Ma OEA-SDAC) is proposed. In the reorganization stage, a restricted matching selection can enhance the reproductivity. In the environment selection stage, a space division and angle-based elimination strategy can effectively improve the convergence and diversity imbalance of its solution set. Through detailed experiments and a comparative analysis of the proposed Ma OEA-SDAC with five other state-of-the-art algorithms on classical benchmark problems, the effectiveness of Ma OEA-SDAC in solving high-dimensional optimization problems has been verified.

关键词： reasonable balance many-objective evolutionary algorithm convergence environment selection stage angle culling strategy elimination strategy spatial division rich diversity Pareto optimisation angle-culling strategy diversity imbalance restricted matching selection specific project evolutionary computation fast convergence reorganization stage MaOEA-SDAC space division optimal solutions

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many-objective evolutionary algorithm based on three-way decision

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EGYPTIAN INFORMATICS JOURNAL 2023年第3期24卷

作者： Cui, Zhihua Li, Bingting Lan, Zhuoxuan Xu, Yubin Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China

In recent years, many-objective optimization problems have been widely used. however, with the increase of the number of objectives, the difficulty of solving increases exponentially, and the imbalance between convergence and diversity becomes more serious. In view of the above problems, this paper combines the idea of three-way decision, redesigns the environment selection strategy, and proposes a many-objective optimization algorithm based on three-way decision. Firstly, the distance from the individual to the ideal point is used as an index to measure individual convergence, the minimum distance from the individual to other solutions is used as an indicator to measure individual diversity, and the individuals with good convergence and good diversity are selected separately by combining the thresholds of the three-way decision;and secondly, A dynamic threshold acquisition method is designed to further improve the performance of the algorithm;Finally, it is proved that the algorithm can effectively balance convergence and diversity through tests of different data sets, so as to verify the feasibility and effectiveness of the algorithm & COPY;2023 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： many-objective optimization problems many-objective evolutionary algorithm Three-way decision Convergence Diversity

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Improved framework of many-objective evolutionary algorithm to handle cloud detection problem in satellite imagery

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IET IMAGE PROCESSING 2020年第17期14卷 4795-4807页

作者： Gupta, Rachana Nanda, Satyasai Jagannath Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur 302017 Rajasthan India

Automatic cloud detection algorithm based on supervised learning approach has emerged due to its effectiveness in extracting weather information in satellite images. However, algorithm requires field-expert intervention with huge database of training samples to evaluate its clustering performance. Moreover, lacking in availability of labelled data makes difficult to train the input samples. Therefore, this article puts forward unsupervised many-objective evolutionary clustering technique to discriminate cloudy regions on varying characteristic of underlying surfaces. The study begins with the modification in search capability of theta-NSGA-III optimisation algorithm by incorporating penalised vector angle concept in associate operator. The analysis of proposed approach has been carried out on benchmark many-objective DTLZ test problems, compared against original theta-NSGA-III and NSGA-III algorithms. The proposed modified theta-NSGA-III has been further utilised as clustering technique to solve unsupervised cloud detection problem. Optimal centroid vector for clustering using proposed approach is obtained through modified crossover operator, mutation operator and environmental selection method. Experimental results reveal that proposed approach outperforms comparative many-objective algorithms, MOEA/D and NSGA-III for Landsat 8, MODIS and NOAA satellite images with lower classification average error of 2.44% in cloud detection for most of the evaluated test cases.

关键词： modified crossover operator NSGA-III optimisation algorithm many-objective evolutionary clustering technique training samples supervised learning approach atmospheric techniques underlying surfaces search capability NSGA-III algorithms weather information Computer vision and image processing techniques Data and information environmental selection method optimal centroid vector geophysical image processing Optical, image and video signal processing unsupervised cloud detection problem field-expert intervention satellite imagery image classification improved framework Atmospheric, ionospheric and magnetospheric techniques and equipment benchmark many-objective DTLZ test problems Cloud physics cloudy regions vector angle concept clouds clustering performance labelled data automatic cloud detection algorithm supervised learning huge database Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Geography and cartography computing many-objective evolutionary algorithm acquisition, processing, storage and dissemination in geophysics

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A many-objective evolutionary algorithm with epsilon-indicator direction vector

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APPLIED SOFT COMPUTING 2019年 76卷 326-355页

作者： Yang, Yun Luo, Jianping Huang, Lei Liu, Qiqi Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China

The major difficulty in multi-objective optimization evolutionary algorithms (MOEAs) is how to find an appropriate solution which is able to converge towards the true Pareto Front with high diversity. In order to strengthen the selection pressure of the algorithms, indicator-based algorithms have been proposed to handle many-objective optimization problems (MaOPs), among which binary addition quality indicator I epsilon+ is superior to other indicators in terms of low computational complexity. However it often has edge effects which degrade the performance of MOEA. In this work, we devise a new MOEA approach, which is able to combine binary addition quality indicator I epsilon+ with direction vector (EDV), to address MaOPs. At the same time, an efficient resource allocation strategy is developed to improve the diversity distribution of solutions. Simulation results are presented to show that EDV outperforms state-of-the-art approaches in all problems considered in this paper, and takes a great advantage in solving the black box problem. (C) 2018 Elsevier B.V. All rights reserved.

关键词： many-objective evolutionary algorithm many-objective test problems Binary quality indicator Uniform direction vector

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A many-objective evolutionary algorithm for solving computation offloading problems under uncertain communication conditions

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COMPUTER COMMUNICATIONS 2024年 213卷 22-32页

作者： Li, Qi Shi, Zhenyu Xue, Zhaoyu Cui, Zhihua Xu, Yubin Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China

Computation offloading is a critically important technology in the field of edge computing, enabling improved conditions for task computation. Existing studies on computation offloading largely focus on cooperative offloading and resource allocation. The impact of factors such as uncertainty during task transmission and the different task characteristics on the offloading location is ignored. To address the above issues, this paper designs a many-objective optimization computation offloading model (MaOCO) under uncertainty to cope with offloading requirements. The model formulates channel transmission level strategies and considers five objectives: latency, cost, energy consumption, load balancing, and user satisfaction. A many-objective evolutionary algorithm with deviation selection (MaOEA-DS) is designed to obtain effective offloading strategies. In the environment selection, the deviation is calculated using the concept of variance to choose solutions closer to the origin for the last layer, which further improves the convergence of the algorithm. The simulation results show that MaOEA-DS outperforms other evolutionary algorithms in IGD, GD, SP, and HV.

关键词： Edge Computing many-objective evolutionary algorithm Task offloading Uncertainty

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An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty

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INFORMATION SCIENCES 2022年 583卷 56-72页

作者： Zhang, Zhixia Zhao, Mengkai Wang, Hui Cui, Zhihua Zhang, Wensheng Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing Peoples R China

Task scheduling is an important research direction in cloud computing. The current research on task scheduling considers mainly the design of scheduling strategies and algorithms and rarely gives attention to the influences of uncertain factors, such as the network bandwidth and millions of instructions per second (MIPS), on the scheduling process. The network bandwidth and MIPS directly affect the performance of a virtual machine (VM), which further influences the scheduling performance. In this paper, uncertain factors are transformed into interval parameters. The make-span, scheduling cost, load balance, and task completion rate are simultaneously considered in the scheduling process. Then, an interval many-objective cloud task scheduling optimization (I-MCTSO) model is designed to simulate real cloud computing task scheduling. To implement this model, an interval many-objective evolutionary algorithm (InMaOEA) is proposed. An interval credibility strategy is employed to improve the convergence performance. The hyper-volume and degree of overlap based on the interval congestion distance strategy are used to increase the population diversity. Simulation results demonstrate the effectiveness and superior performance of InMaOEA in comparision with other algorithms. The proposed approaches can provide decision-makers with an efficient allocation plan for cloud task scheduling. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Interval optimization Interval many-objective optimization many-objective evolutionary algorithm Cloud task scheduling

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Risk design optimization using many-objective evolutionary algorithm with application to performance-based wind engineering of tall buildings

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STRUCTURAL SAFETY 2014年 48卷 1-14页

作者： Li, Gang Hu, Hao Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

Risk design optimization (RDO) is a competent approach for automated performance-based structural design by achieving a balance between safety and economy. Performance-based wind engineering (PBWE) is aimed at improving the life-cycle functionality of wind-sensitive structures, hence could be the very field RDO is tailor-made for. In this paper, we embed PBWE of tall buildings into RDO and tackle some difficulties when integrating them directly. We first formulate an integrated stiffness and vibration control RDO problem, and employ a frequency domain closed-form solution for uncertainty quantification and uncertainty propagation through the excitation-response-performance chain. Then we reveal the multi-objective optimization nature of RDO, and circumvent the difficulties in serviceability loss estimation by replacing scalar total cost with high-dimensional objective vector. Micro multi-objective particle swarm optimization in conjunction with kernel-learning based principle component analysis is employed to solve the corresponding many-objective problem with multiple probabilistic constraints and discrete design variables. The optimization results of CAARC benchmark indicate that we simplify risk-based PBWE of tall buildings from a complex multi-objective decision making process into a relatively easy multi-attribute decision making process. Accordingly, convincing decisions can be made based on the explicit building performance rather than the unreliable loss information. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Risk design optimization Reliability-based design optimization Multi-objective optimization many-objective evolutionary algorithm Performance-based wind engineering Particle swarm optimization

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A many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism

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SWARM AND evolutionary COMPUTATION 2024年 90卷

作者： Yang, Lei Cao, Jiale Li, Kangshun Zhang, Yuanye Xu, Rui Li, Ke South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

In many-objective optimization, both convergence and diversity are equally important. However, in high- dimensional spaces, traditional decomposition-based many-objective evolutionary algorithms struggle to ensure population diversity. Conversely, traditional Pareto dominance-based many-objective evolutionary algorithms face challenges in ensuring population convergence. In this paper, we propose a novel many-objective evolutionary algorithm based on interaction force and hybrid optimization mechanism (MaOEAIH) for effectively addressing the difficulty in balancing convergence and diversity. First, we use the concept of interaction force to simulate the convergence (akin to gravity) and diversity (repulsion) of the population. Subsequently, we design an optimization mechanism that combines decomposition and Pareto dominance to enhance the convergence and diversity of the population separately. Simultaneously, to eliminate dominance resistance solutions, we propose a quartile method based on boundary solutions. Additionally, Random perturbations are also introduced to certain individuals within the population to facilitate their escape from local optima. MaOEAIH is compared with some state-of-the-art algorithms on 31 well-known test problems with 3-15 objectives. The experimental results show that, compared to other algorithms, MaOEAIH not only obtains solution sets of higher quality when dealing with different types of many-objective optimization problems, but also effectively addresses key challenges including insufficient selection pressure, difficulty balancing convergence and diversity, and susceptibility to population entrapment in local optima within many-objective optimization scenarios.

关键词： many-objective evolutionary algorithm Interaction force Hybrid evolutionary mechanism Dominance resistance solutions Perturbation

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A many-objective evolutionary algorithm based on Directional Diversity and Favorable Convergence

A Many-Objective Evolutionary Algorithm based on Directional...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Cheng, Jixiang Yen, Gary G. Zhang, Gexiang Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781479938407

The performances of Pareto-based multi-objective evolutionary algorithms deteriorate severely when solving many-objective optimization problems (MaOPs) mainly due to the loss of selection pressure and inappropriate design in diversity maintenance mechanism. To handling MaOPs, this paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity and favorable convergence (MaOEA-DDFC). In the algorithm, the mating selection based on favorable convergence and Pareto-dominance is applied to strengthen the selection pressure while an environmental selection considering directional diversity and favorable convergence is designed in order to make a good trade-off between diversity and convergence. To validate algorithm performance, seven DTLZ problems with 3, 5, 7 and 10 objectives are tested. Experimental results show that the proposed MaOEA-DDFC performs better than five state-of-the-art MaOEAs in terms of inverted generational distance and hypervolume indicators.

关键词： many-objective evolutionary algorithm directional diversity favorable convergence

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Two-stage adaptive weight vector design method for decomposition based many-objective evolutionary algorithm

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2022年第1-2期39卷 58-69页

作者： Guo, Xiaofang Wang, Yuping Gao, Xiaozhi Xian Technol Univ Sch Sci Xian Shaanxi Peoples R China Univ Eastern Finland Sch Comp Kuopio Finland

In order to acquire the searching information of the irregular shape of the PF accurately, this paper proposes a two-stage adaptive weight vector design approach for decomposition based many objective evolutionary algorithms. Firstly, a preset initial weight vector generation strategy based on the crowding information is proposed to capture the effective area of the distribution of population. Next, the self-organised mapping (SOM) weight vector design method is adopted to generate weight vectors to capture the topological structure of the distribution of PF. In addition, a new composite aggregation function combined Lp metric distance with angle-distance is proposed to improve both convergence and diversity. The performance of our algorithm is examined using a total of 10 problems with both regular and irregular PF. The experimental results show that the proposed method can significantly improve the convergence and diversity performance with irregular shape of PF.

关键词： many-objective evolutionary algorithm two stage crowding self-organised mapping SOM Lp-metric

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