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A hybrid many-objective evolutionary algorithm for flexible job-shop scheduling problem with transportation and setup times

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COMPUTERS & OPERATIONS RESEARCH 2021年 132卷 105263-105263页

作者： Sun, Jinghe Zhang, Guohui Lu, Jiao Zhang, Wenqiang Zhengzhou Univ Aeronaut Sch Management Engn Zhengzhou Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Peoples R China

This paper addresses a many-objective flexible job-shop scheduling problem with transportation and setup times (MaOFJSP_T/S) where the objective is to minimize the makespan, total workload, workload of the critical machine, and penalties of earliness/tardiness. We first present a mathematical model as the representation of the problem, and then establish a network graph model to describe the structural characteristics of the problem and develop a new neighborhood structure. The neighborhood structure defines four move types for different objectives. Next, we propose a hybrid many-objective evolutionary algorithm (HMEA), which is designed to better balance exploitation and exploration. In this algorithm, the tabu search with the neighborhood structure is proposed to improve the local search ability. A reference-point based non-dominated sorting selection is presented to guide the algorithm to search towards the Pareto-optimal front and maintain diversity of solutions. Through three sets of experiments based on 28 benchmark instances, the partial and overall effects of this algorithm are evaluated. The experimental results demonstrate the effectiveness of the proposed HMEA in solving the MaOFJSPT/S.

关键词： many-objective evolutionary algorithm Flexible job-shop scheduling problem Transportation and setup times Neighborhood structure Reference-point

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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 incorporating decision maker's preference and its application to management of the electricity distribution network

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JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2019年第3-4期26卷 165-186页

作者： Sekizaki, Shinya Nishizaki, Ichiro Hayashida, Tomohiro Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima Japan

This paper proposes a step-by-step decision making approach that incorporates preferences of a decision maker into a many-objective evolutionary algorithm. The algorithm is applied to a challenging many-objective optimization problem (MaOP) involving integer objective values, tight constraints, heavy computational burden, and limited knowledge about the true Pareto front. The proposed method consists of five steps. (a) The entire Pareto front with high dimensions is roughly approximated by a small set of nondominated solutions. (b) On the basis of the approximated Pareto front, the decision maker specifies a preference. (c) Reference points are distributed through part of the objective space using the preference information. (d) The approximation accuracy of the Pareto front in the region of interest is improved by local search using the reference points. (e) The most preferred solution from a set of the nondominated solutions is chosen. The evolutionary algorithms employed in Steps (a) and (d) have an improved normalization procedure and selection mechanism for the MaOP involving integer objective value(s) and the tight constraints. Steps (b) and (e) use a graphical-user-interface-based interactive tool we developed to efficiently narrow down to the preferred solution(s) among the high-dimensional, nondominated solutions. This alleviates the cognitive burden on the decision maker and therefore aids the decision maker in handling MaOPs. In the computational experiment, we choose the real-world application of reconfiguring an electricity distribution network. We demonstrate that the proposed approach can provide satisfying solutions to a decision maker.

关键词： electricity distribution network management interactive decision aid tool many-objective evolutionary algorithm preference of a decision maker

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evolutionary many-objective algorithm with improved growing neural gas and angle-penalized distance for irregular problems

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APPLIED INTELLIGENCE 2023年第17期53卷 19892-19921页

作者： Gu, Qinghua Pang, Dejun Wang, Qian Xian Univ Architecture & Technol Sch Management Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & De Xian 710055 Peoples R China Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China

For solving complex many-objective optimization problems, the many-objective evolutionary algorithms may require new distance measures and more enormous selection pressures to guide solutions to the Pareto front. In this case, solving irregular Pareto front problems with growing neural gas networks as reference vectors is a great challenge. This paper proposes an algorithm with improved growing neural gas and angle-penalized distance to overcome the difficulty: First, a new parameter, the proportion of the solutions relatively close to the input data, is introduced to the angle-penalty distance, boosting the influence on the entire environment selection and enhancing the effect for Pareto front learning. Second, the edge-acceleration strategy enhances the ability of growing neural gas to match Pareto fronts and adapt to rapidly changing input. Finally, by adjusting the nodes deletion strategy in the search procedure, more promising solutions may be maintained to optimize the solutions' distribution. In experiments tackling problems with irregular Pareto fronts, the proposed algorithm performs competitively in contrast to the other six excellent algorithms.

关键词： many-objective evolutionary algorithm Irregular Pareto front Growing neural gas Angle-penalized distance

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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 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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A Novel objective Grouping evolutionary algorithm for many-objective Optimization Problems

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第6期34卷

作者： Guo, Xiaofang Wang, Xiaoli Xian Technol Univ Sch Sci Xuefu Rd Xian Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol South Taibai Rd Xian Shaanxi Peoples R China

The thorniest difficulties for multi-objective evolutionary algorithms (MOEAs) handling many-objective optimization problems (MaOPs) are the inefficiency of selection operators and high computational cost. To alleviate such difficulties and simplify the MaOPs, objective reduction algorithms have been proposed to remove the redundant objectives during the search process. However, those algorithms can only be applicable to specific problems with redundant objectives. Worse still, the Pareto solutions obtained by reduced objective set may not be the Pareto solutions of the original MaOPs. In this paper, we present a novel objective grouping evolutionary algorithm (OGEA) for general MaOPs. First, by dividing original objective set into several overlapping lower-dimensional subsets in terms of interdependence correlation information, we aim to separate the MaOPs into a number of sub-problems so that each of them can be able to preserve as much dominance structure in the original objective set as possible. Subsequently, we employ the nondominated sorting genetic algorithm II (NSGA-II) to generate Pareto solutions. Besides, instead of nondominated sorting on the whole population, a novel dual selection mechanism is proposed to choose individuals either having high ranks in subspaces or locating sparse region in the objective space for better proximity and diversity. Finally, we compare the proposed strategy with the other two classical space partition methods on benchmark DTLZ5 (I, M), DTLZ2 and a practical engineering problem. Numerical results show the proposed objective grouping algorithm can preserve more dominance structure in original objective set and achieve better quality of Pareto solutions.

关键词： many-objective evolutionary algorithm objective grouping interdependence relation dual selection mechanism

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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

作者： Jixiang Cheng Gary G. Yen Gexiang Zhang School of Electrical Engineering Southwest Jiaotong University School of Electrical and Computer Engineering Oklahoma State University

ISBN: (纸本)9781479938414

关键词： many-objective evolutionary algorithm Directional diversity Favorable convergence

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many-objective joint optimization for dependency-aware task offloading and service in mobile

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第5期18卷 1238-1259页

作者： Shi, Xiangyu Zhang, Zhixia Cui, Zhihua Cai, Xingjuan Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependencyaware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub -task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.

关键词： Mobile Edge Computing (MEC) Dependency Aware Task Offloading Service Caching Multi-user Resource Constraint many-objective evolutionary algorithm

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many-objective emergency aided decision making based on knowledge graph

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APPLIED INTELLIGENCE 2024年第17-18期54卷 7733-7749页

作者： Li, Xiaoxuan Zhao, Tianhao Wen, Jie Cai, Xingjuan Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

After emergencies occur, decision-makers can reference historical cases with similar causes to take similar emergency response measures. However, information about emergencies is usually recorded and stored in textual form, and it is difficult for decision-makers to obtain effective information from large amounts of textual data and make decisions that balance various factors. To address these issues, this paper presents an emergency assisted decision-making model based on a knowledge graph and many-objective optimization. First, we preprocess the information by extracting entities to construct a knowledge graph of emergencies to make the event information more structured and easier to use. A knowledge graph is also used to narrow the range of matching historical events. Second, we construct a many-objective model with four objectives: similarity, diversity, processing time, and resource cost. Finally, combining the model characteristics, we design genetic operations for duplicate location matching and substitution removal strategies to obtain the nonrepetitive VaEA algorithm. This algorithm is used to optimize the model and generate a list of reference cases in the reduced matching range to provide rescue strategy suggestions for the current situation. The experimental results show that the algorithm outperforms the comparison algorithms under all four evaluation metrics. This indicates that the method in this paper can match the higher-quality historical emergency solutions applicable to the current decision-making situation in the case base and provide support for decision-makers to respond reasonably in emergencies.

关键词： Emergency-assisted decision-making Knowledge graph Optimization many-objective evolutionary algorithm

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