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An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery

KNOWLEDGE-BASED SYSTEMS 2021年 218卷 106881-106881页

作者： Zou, Wen-Qiang Pan, Quan-Ke Wang, Ling Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

This paper investigates a new automatic guided vehicle scheduling problem with pickup and delivery from the goods handling process in a matrix manufacturing workshop with multi-variety and small-batch production. The problem aims to determine a solution that maximizes customer satisfaction while minimizing distribution cost. For this purpose, a multi-objective mixed-integer linear programming model is first formulated. Then an effective multi-objective evolutionary algorithm is developed for solving the problem. In the algorithm, a constructive heuristic is presented and incorporated into the population initialization. A multi-objective local search based on an ideal-point is used to enforce the exploitation capability. A novel two-point crossover operator is designed to make full use of valuable information collected in the non-dominated solutions. A restart strategy is proposed to avoid the algorithm trapping into a local optimum. At last, a series of comparative experiments are implemented based on a number of real-world instances from an electronic equipment manufacturing enterprise. The results show that the proposed algorithm has a significantly better performance than the existing multi-objective algorithms for solving the problem under consideration. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Matrix manufacturing workshop Automated guided vehicles Scheduling Pickup and delivery multi-objective evolutionary algorithm

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Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

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ENERGY 2024年 311卷

作者： Ali, Aamir Bughio, Ateeq-u-Rehman Abbas, Ghulam Keerio, M. U. Mugheri, N. H. Memon, Shaina Saand, A. S. Quaid e Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67450 Sindh Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is *** scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for smallscale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary *** study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

关键词： Distribution network Distributed generation Battery energy storage system multi-objective evolutionary algorithm

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An interactive filter-wrapper multi-objective evolutionary algorithm for feature selection

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SWARM AND evolutionary COMPUTATION 2021年 65卷 100925-100925页

作者： Liu, Zhengyi Chang, Bo Cheng, Fan Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal ProcMinist Edu Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China

As an important task in data mining, feature selection can improve the performance of classification by eliminating the redundant or irrelevant features in original data. It is mainly divided into filter method and wrapper method, and each one has its own advantages. To make full use of the advantages of two methods, in this paper, an interactive filter-wrapper multi-objective evolutionary algorithm, named GR-MOEA is proposed, where guiding and repairing strategies are used to select feature subsets with high quality. To be specific, a wrapper population and a filter population are evolved simultaneously in the proposed algorithm. To utilize the merits of two populations, an interactive scheme is designed, which includes a wrapper to filter guiding strategy and a filter to wrapper repairing strategy. The guide strategy is to use the good solutions in the wrapper population to steer the filter population towards a better direction. While in the repairing strategy, some features in the wrapper population are repaired by the useful information in filter population, which can avoid the trapping of local optimum in wrapper population. To further enhance the performance of GR-MOEA, two effective initialization strategies are also developed. Empirical studies are conducted by comparing the proposed algorithm with several state-of-the-art on different datasets, and the experimental results demonstrate the superiority of GR-MOEA over the comparison methods in obtaining the feature subsets with higher qualities.

关键词： Feature selection multi-objective evolutionary algorithm Guiding strategy Repairing strategy Initialization strategy

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An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots

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KNOWLEDGE-BASED SYSTEMS 2022年 238卷 1页

作者： Zhang, Biao Pan, Quan-ke Meng, Lei-lei Lu, Chao Mou, Jian-hui Li, Jun-qing Liaocheng Univ Sch Comp Sci Liaocheng 25200 Shandong Peoples R China Shanghai Univ Sch Mech Engn & Automat Shanghai 200072 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Yantai Univ Sch Mech Elect & Automobile Engn Yantai 264005 Peoples R China

Lot streaming is the most widely used technique to facilitate overlapping of successive operations. Inspired by real-world scenarios, this paper studies a multi-objective hybrid flowshop scheduling problem with consistent sublots, aiming to simultaneously optimize two conflicting objectives: the makespan and total number of sublots. Considering the setup and transportation operations, a multi-objective mixed integer programming model is developed and the trade-off between the two objectives is evaluated. Because of the NP-hard property of the addressed problem, metaheuristics are suggested. It is well known that the performance of metaheuristics is highly dependent on the setting of algorithmic parameters, referred to as numerical and categorical parameters. However, the traditional design process might be biased by previous experience. To eliminate these issues, an automated algorithm design (AAD) methodology is introduced to conceive a multi-objective evolutionary algorithm (MOEA) in a promising framework. The AAD enables designing the algorithm by automatically determining parameters and their combinations with minimal user intervention. With regard to the problem-specific characteristics and the employed algorithm framework, for the categorical parameters, including decomposition, solution encoding and decoding, solution initialization and neighborhood structures, several operators are designed specifically. Along with the numerical parameters, these categorical parameters are determined and combined using the designed iterated racing procedure. Comprehensive computational results demonstrate that the automated MOEA outperforms other state-of-the-art MOEAs for the addressed problem. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Hybrid flowshop scheduling Lot streaming Consistent sublots multi-objective evolutionary algorithm Automatic algorithm design

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A decomposition-based multi-objective evolutionary algorithm using infinitesimal method

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wang, Jing Mei, Shunce Liu, Changxin Peng, Hu Wu, Zhijian Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 Peoples R China JiuJiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been extensively employed to address a diverse array of real-world challenges and has shown excellent performance. However, the initial collection of aggregate weight vectors proves unsuitable for multi-objective optimization problems (MOPs) featuring intricate Pareto front (PF) structures, and the solving performance will be greatly affected when MOEA/D solves these irregular MOPs. In light of these challenges, a refined MOEA/D algorithm utilizing infinitesimal method is proposed. This algorithm incorporates the notion of global decomposition stemming from infinitesimal method to streamline the feature information of PF, thereby facilitating the adjustment of the weight vector towards optimal distribution. Consequently, enhancements in resource allocation efficiency and algorithmic performance are achieved. In the empirical investigation, the algorithm's performance is tested on 28 benchmarks from ZDT,DTLZ and WFG test suits. Wilcoxon's rank-sum test and Fredman's test were carried out on performance metrics, which proved that the proposed MOEA/D-DKS was superior to other comparison algorithms.

关键词： multi-objective evolutionary algorithm Decomposition Infinitesimal method Weight vector adjustment

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An improved clustering based multi-objective evolutionary algorithm for influence maximization under variable-length solutions

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KNOWLEDGE-BASED SYSTEMS 2022年第0期256卷

作者： Biswas, Tarun K. Abbasi, Alireza Chakrabortty, Ripon K. Univ New South Wales Sch Engn & Informat Technol Canberra 2600 Australia Jashore Univ Sci & Technol Dept Ind & Prod Engn Jashore 7408 Bangladesh

Knowledge of influential actors on real networks is essential for designing policies for boosting the spread of positive things (e.g., news and innovation) and preventing the spread of negative things (e.g., disease and misinformation). Finding an optimum set of nodes with maximum influence, known as Influence Maximization (IM), aims to achieve this, which requires consideration of both magnitude of the influence spread and other important factors such as the cost and size of the seed set. However, most previous research has viewed the IM problem as a single objective problem, wherein decision-makers are required to make decisions on other criteria without having a clear view of them. In this study, the IM problem is formulated as a multi-objective Influence Maximization Problem (MIMP) with three conflicting objectives and real-world constraints. An improved version of theta-Dominance based evolutionary algorithm, named as theta DEA-II, is proposed to solve the MIMP. To achieve a better balance between divergence and convergence of the proposed theta DEA-II, a gap-based selection operator is introduced along with some modifications in the sorting and normalization process. Moreover, because the proposed MIMP deals with variable-length solutions, all of the investigated algorithms, including the baseline algorithms, are constructed with a novel crossover mechanism capable of producing offsprings that are different in size from their parents. Experimental results on six real-world datasets demonstrate that the proposed MIMP formulation as well as the theta DEA-II algorithm are effective for finding an optimal solution. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Influence maximization multi-objective evolutionary algorithm Variable-length solutions ?-dominance Social network analysis

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Automatic Optimization of Guidance Guardrail Layout Based on multi-objective evolutionary algorithm

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Complex System Modeling and Simulation 2024年第4期4卷 353-367页

作者： Wei-Li Liu Yixin Chen Xiang Li Jinghui Zhong Rongjun Chen Hu Jin School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510006China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China School of Electrical Engineering Hanyang UniversityAnsan 15588Republic of Korea

Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public ***,existing methods of the guardrail layout mainly rely on manual design or mathematical models,which are not flexible or effective enough for crowd control in large public *** address this limitation,this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary *** paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail *** addition to optimizing pedestrian passage efficiency and safety,the paper also considers the situation of pedestnan counterflow,making the guardrai layout more *** results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion,as well as its significant improvements in passage effciency and prevention of pedestrian counte rflow.

关键词： automatical guardrall layout optimization,crowd simulation multi-objective evolutionary algorithm

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A matheuristic-based multi-objective evolutionary algorithm for flexible assembly jobs shop scheduling problem in cellular manufacture

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SWARM AND evolutionary COMPUTATION 2024年 87卷

作者： Hu, Yifan Zhang, Liping Wang, Qiong Zhang, Zikai Tang, Qiuhua Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Sch Math & Stat Wuhan 430074 Peoples R China

The flexible assembly job shop scheduling problem (FAJSP) and its extensions have received increased attention. However, multi-objective FAJSP in cellular manufacture (FAJSP-CM), a real-world extension of FAJSP with important applications in the mass customization of complex products, is barely considered in most previous studies. Hence, this study first addresses the multi-objective FAJSP-CM problem via a mixed-integer linear programming model (MILP). Then, a matheuristic-based multi-objective evolutionary algorithm (MMOEA), with a matheuristic decoding method, knowledge-guided initialization and learning-based local intensification method, is presented to efficiently tackle the FAJSP-CM problem in polynomial-time. The matheuristic decoding method, which hybrids a decomposition linear programming model (D-LP) that can quickly and optimally allocate the execution times of all operations, to search for the optimal solution in a specific space. The knowledge-guided initialization is extended to get a high-quality and great-diversity initial population for accelerated convergence. Meanwhile, the learning-based local intensification method is developed to adaptively coordinate five problem-specific local search operators to enhance the exploitation ability of the MMOEA algorithm. Finally, comprehensive experiments based on 720 instances are conducted to evaluate the proposed MILP model and MMOEA algorithm. The results of the first experiments illustrate the MILP model can obtain the Pareto solutions of small-scale instances. The remaining experiments demonstrate that the proposed MMOEA algorithm outperforms its competitors by 0.21 and 0.72 in hypervolume ratio and coverage indictors, along with approximately a 25 % improvement in success rate.

关键词： Flexible assembly job shop scheduling problem Cellular manufacture Matheuristic multi-objective evolutionary algorithm

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A multi-objective evolutionary algorithm based on dimension exploration and discrepancy evolution for UAV path planning problem

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INFORMATION SCIENCES 2024年 657卷

作者： Xu, Xiuju Xie, Chengyu Luo, Zongfu Zhang, Chuanfu Zhang, Tao Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou 510275 Peoples R China

Path planning is a crucial process for unmanned aerial vehicles (UAVs) and involves finding a path that is both short and safe. However, with the ever-increasing complexity of the environment, solving the UAV path-planning problem is challenging. Traditional path-planning methods cannot handle conflicting goals effectively, and existing objective methods lack targeted exploration mechanisms, resulting in unsatisfactory outcomes. By modeling the UAV path-planning problem via multi-objective optimization, this study designed a reasonable objective function composition for the model and considered obstacle avoidance as a hard constraint to satisfy the actual situation. A multi-objective evolutionary algorithm based on dimensional exploration and discrepancy evolution (MOEA-2DE) is presented. In particular, MOEA-2DE utilizes dimensional perturbation to identify key dimensions to facilitate prior exploration and enhance the targeted search. An adaptive evolution strategy based on population discrepancy was employed to assess the evolution process, and various methods were adopted to balance convergence and diversity. The effectiveness of the MOEA-2DE was demonstrated through the design of two intricate terrain sets and comparisons with various classic and state-of-the-art multi-objective evolutionary algorithms (MOEAs), including those designed for UAV path planning across multiple metrics. The results verify the superiority of MOEA-2DE in terms of both convergence speed and final effect.

关键词： Path planning UAV multi-objective optimization multi-objective evolutionary algorithm

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Humanoid robot arm performance optimization using multi objective evolutionary algorithm

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2014年第4期12卷 870-877页

作者： Mohamed, Zulkifli Kitani, Mitsuki Kaneko, Shin-ichiro Capi, Genci Toyama Univ Fac Engn Dept Elect & Elect Syst Engn Toyama 9308555 Japan Univ Teknol MARA Fac Mech Engn Shah Alam 40450 Selangor Malaysia Toyama Natl Coll Technol Dept Elect & Control Syst Engn Toyama Japan

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria;minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.

关键词： Genetic algorithm mobile humanoid robot multi-objective evolutionary algorithm robot arm motion generation

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