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multi-objective evolutionary algorithm based on RBF network for solving the stochastic vehicle routing problem

INFORMATION SCIENCES 2022年 609卷 387-410页

作者： Niu, Yunyun Shao, Jie Xiao, Jianhua Song, Wen Cao, Zhiguang China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China Shandong Univ Inst Marine Sci & Technol Shandong 250100 Peoples R China Singapore Inst Mfg Technol Singapore 138634 Singapore

Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi -objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi -objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function net-work (RBFN) is exploited to learn the potential knowledge of individuals, generate hypoth-esis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into account the non -dominated relationship between individuals. Moreover, integrated with a specific non -dominated sorting strategy, i.e., ENS-SS, along with several effective heuristic operations, the proposed algorithm performs favorably for solving the MO-VRPSD. The experimental results based on the modified Solomon benchmark instances verified the effectiveness of the respective components, and the superiority to other multi-objective evolutionary algorithms. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Vehicle routing problem Stochastic demand Learnable evolution model multi -objective evolutionary algorithm Radial basis function network Vehicle routing problem Stochastic demand Learnable evolution model multi -objective evolutionary algorithm Radial basis function network

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multi-objective evolutionary algorithm-enabled multi-stage collaborative scheduling for automotive production

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 191卷

作者： Zhang, Xiangfei Li, Congbo Zhang, Jing Yang, Miao Wu, Wei Chongqing Univ State Key Lab Mech Transmission Chongqing 400044 Peoples R China

In the automotive industry, considering all the process workshops as a whole in terms of production scheduling becomes much more significant for the enhancement and optimization of overall productivity, efficiency, resource utilization, and coordination among factories. However, the complicated operational interdependencies between workshops make it hard to acquire a global objective. This paper aims to model the collaborative scheduling problem for a multi-stage automotive production process first, involving critical decision variables from four main workshops, stamping, welding, painting, and assembling. Then, the multi-objective evolutionary algorithm based on Pareto optimal subspace learning (MOEA/PSL) associated with an encoding and decoding strategy based on a random key is designed to solve the model for minimizing the total cost and weighted tardiness. Finally, a real-life case study is carried out to illustrate the effectiveness and superiority of the proposed method via experimental comparison using practical data and simulated instances for further analysis.

关键词： multi -stage automotive manufacturing Collaborative production scheduling multi -objective evolutionary algorithm Pareto optimal subspace learning

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Adaptive surrogate-assisted multi-objective evolutionary algorithm using an efficient infill technique

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SWARM AND evolutionary COMPUTATION 2022年 75卷

作者： Wu, Mengtian Wang, Lingling Xu, Jin Hu, Pengjie Xu, Pengcheng Hohai Univ State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China Hohai Univ Coll Agr Sci & Engn Nanjing Peoples R China

Surrogate-assisted multi-objective evolutionary algorithms have become increasingly popular for solving computationally expensive problems, profiting from surrogate modeling and infill approaches to reduce the time cost of optimization. Most existing algorithms have specified the type of surrogate model before a run and keep the type static during the optimization process. However, a sole surrogate model may not consistently perform well for all problems without any prior knowledge. In this context, this paper proposes an adaptive technique for surrogate models with multiple radial basis functions (RBFs), as the technique can dynamically establish the most promising RBF for each objective, thereby enhancing the reliability of surrogate prediction. Moreover, multi -objective evolutionary algorithms (MOEAs) that are employed as optimizers for infilling criteria can highly affect the search behavior of a surrogate-assisted evolutionary algorithm. The proposed infill technique develops a crowding distance-based prescreening operator to embed various MOEAs. Two techniques collaboratively promote the convergence, coverage, and diversity of the predicted Pareto front. Representative benchmark problems and a structural optimization problem are given to show the effectiveness of the algorithm that em-ploys these techniques. Empirical experiments demonstrate that the proposed algorithm significantly out-performs other state-of-the-art algorithms in most cases.

关键词： Expensive optimization problem Surrogate -assisted evolutionary algorithm multi -objective evolutionary algorithm Radial basis functions Structural designs

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multi-population cooperative multi-objective evolutionary algorithm for sequence-dependent group flow shop with consistent sublots

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期237卷

作者： Zhang, Yuanyuan Li, Junqing Xu, Ying Duan, Peiyong Liaocheng Univ Sch Comp Liaocheng 252059 Peoples R China HengXing Univ Sch Informat Engn Qingdao 266100 Peoples R China Yunan Normal Univ Sch Math Kuming 650500 Peoples R China Qilu Univ Technol Sch Informat Jinan 250014 Peoples R China

Inspired by the production model of pressure vessels for spacecraft, i.e., tanks and cylinders, this study addresses the sequence-dependent group flow shop scheduling problem with consistent sublots (SDGFSP_CS) to minimize makespan and total energy consumption. In the problem under consideration, there are several coupling subproblems, namely, the group sequencing, job sequencing, lot assignment, and machine speed assignment. To solve these problems, a multi-population cooperative multi-objective evolutionary algorithm (MPCMOEA) is proposed. In the MPCMOEA, a hybrid initial method that combines two problem-specific heuristics is designed to generate high-quality initial solutions. Then, considering the problem features, a cooperative mechanism considering the co-evolution of multi-population and the archive set is designed to accelerate the optimization process. In the co-evolutionary stage, to deepen the exploitation ability of local search, an enhanced search with multiple problem-specific operators is implemented. Furthermore, a re-initialization method is developed to improve the global search abilities. Finally, 27 different scale instances are generated for a series of numerical experiments. For the hypervolume and inverse generational distance metrics, MPCMOEA gets 20/27 and 21/27 optimal values, respectively. It verifies that the MPCMOEA outperforms efficient algorithms in terms of the diversity and convergence performance.

关键词： Group flow shop scheduling Sequence -dependent setup time multi -population multi -objective evolutionary algorithm Consistent sublots

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Research on decomposition-based multi-objective evolutionary algorithm with dynamic weight vector

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JOURNAL OF COMPUTATIONAL SCIENCE 2024年 81卷

作者： Zhao, Jiale Huang, Xiangdang Li, Tian Yu, Huanhuan Fei, Hansheng Yang, Qiuling Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hainan Univ Innovat Platform Academicians Hainan Prov Haikou 570228 Peoples R China

In recent years, multi -objective evolutionary algorithm based on decomposition has gradually attracted people 's interest. However, this algorithm has some problems. For example, the diversity of the algorithm is poor, and the convergence and diversity of the algorithm are unbalanced. In addition, users don 't always care about the entire Pareto front. Sometimes they may only be interested in specific areas of entire Pareto front. Based on the above problems, this paper proposes a decomposition -based multi -objective evolutionary algorithm with dynamic weight vector (MOEA/D-DWV). Firstly, a weight vector generation model with uniform distribution or preference distribution is proposed. Users can decide which type of weight vector to generate according to their own wishes. Then, two combination evolution operators are proposed to better balance the convergence and diversity of the algorithm. Finally, a dynamic adjustment strategy of weight vector is proposed. This strategy can adjust the distribution of weight vector adaptively according to the distribution of solutions in the objective space, so that the population can be uniformly distributed in the objective space as much as possible. MOEA/D-DWV algorithm is compared with 9 advanced multi -objective evolutionary algorithms. The comparison results show that MOEA/D-DWV algorithm is more competitive. Data availability: Data will be made available on request.

关键词： multi -objective evolutionary algorithm Decomposition Dynamic weight vector Preference distribution Combination evolution operator

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A dual-population and multi-stage based constrained multi-objective evolutionary

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INFORMATION SCIENCES 2022年 615卷 557-577页

作者： Raju, M. Sri Srinivasa Dutta, Saykat Mallipeddi, Rammohan Das, Kedar Nath Natl Inst Technol Silchar Dept Math Silchar India Brainware Univ Sch Computat & Appl Sci Dept Math Kolkata India Kyungpook Natl Univ Sch Elect Engn Dept Artificial Intelligence Daegu South Korea

The existence of constrained multi-objective optimization problems (CMOPs) in real-world applications motivate researchers to focus more on developing constrained multi-objective evolutionary algorithms (CMOEAs). Due to the presence of constraints, an efficient constraint handling technique (CHT) is required in CMOEA to balance the constraint satisfaction and optimization of objective functions. Recently, different fitness based, ranking based, multi-population and multi-staged evolutionary approaches are proposed to handle CMOPs. However, most of the approaches still struggle while handling CMOPs with discontinuous feasible regions or whose feasible regions consist infeasible barriers. To overcome these issues, we propose a novel Dual-Population and multi-Stage based Constrained multi-objective evolutionary algorithm which is termed as CMOEA-DPMS. In CMOEADPMS, two populations are used to explore the search space and feasible regions. Along with two populations, an archive is also employed to store feasible, well converged and distributed solutions. To employ appropriate mating selection and environmental selection strategies according to the evolution of the populations, evolutionary process is divided into several stages. A strategy decider mechanism is proposed to determine the appropriate mating and environmental selections depending on the status of the population. In addition, a novel CHT named decomposition based constraint non-dominating sorting (DCDSort) is proposed by combining decomposition based selection with traditional constraint non-dominating sorting to maintain feasibility, convergence and diversity. The proposed algorithm is evaluated on five recent and popular test suites along with 36 realworld constrained multi-objective optimization problems against eight state-of-the-art algorithms. The empirical results suggests that CMOEA-DPMS is significantly superior or comparable to the considered algorithms and can tackle all kinds of CMOPs.(c) 2022 El

关键词： Constraint Handling multi -objective evolutionary algorithm Optimization Decomposition Dual population

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Matheuristic and learning-oriented multi-objective artificial bee colony algorithm for energy-aware flexible assembly job shop scheduling problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Hu, Yifan Zhang, Liping Zhang, Zikai Li, Zixiang Tang, Qiuhua Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China

With the increase of mass customization, flexible job shop scheduling problem considering assembly stage has widely existed in many manufacturing industries, such as die-casting mould factories. This problem is to find a reasonable machine assignment and operation sequence both in fabrication and assembly stages and simultaneously maximize production efficiency. In reality, energy shortages and environmental pollution have given an impetus to the development of energy-aware production scheduling problems. In this study, we address an energy-aware flexible assembly job shop scheduling problem (EFAJSP) with the objectives of minimizing flow time and energy consumption and first develop a mixed-integer linear programming (MILP) model to solve EFAJSP problem. Then, the model-specific characteristics are extracted and applied to a matheuristic decoding method for exploring the Pareto optimal solution. Due to the complexity of EFAJSP problem, a matheuristic and learning-oriented multi-objective artificial bee colony algorithm (MLABC), which combines the advantages of mathematical programming, reinforcement learning and meta-heuristic algorithm, is proposed. In addition, an initialization, destruction/construction operator and population update operator are proposed and work together to improve the exploration and exploitation performance of the proposed MLABC. Finally, numerical experimental results demonstrate the effectiveness of the proposed MILP model and the superiority of the MLABC over other algorithms in the literature.

关键词： Flexible assembly job shop scheduling problem multi -objective evolutionary algorithm Reinforcement learning Energy consumption Flow time

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A comprehensive risk assessment view on interval type-2 fuzzy controller for a time-dependent HazMat routing problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期305卷 685-707页

作者： Zandieh, Fatemeh Ghannadpour, Seyed Farid Iran Univ Sci & Technol Dept Ind Engn Tehran *** Iran

Hazardous material transportation is an integral part of industries that pose significant risks. Hazardous material transportation risk of is proportional to the volume of materials transferred, the length of the link, and the population density, which varies over time. By modeling the effects of time on population density, this paper presents a multi-objective time-dependent hazardous materials routing problem for efficiently managing hazardous material transportation. The proposed model's objectives include the re-duction of transportation risks and travel costs. Given the uncertainty associated with hazardous materi-als routing, an interval type-2 fuzzy logic controller is used to estimate risk;its inputs include population density, vehicle load, link length, and time of day. A type-2 fuzzy multi-objective evolutionary algorithm based on decomposition is used to optimize the proposed model and an adaptive large neighborhood search to enhance the local neighbor search mechanism. Additionally, by utilizing the nadir reference point, the distribution of Pareto front approximation is improved. Furthermore, a new metric is intro-duced to evaluate the dispersion of Pareto front approximation. The proposed method is compared to three other state-of-the-art evolutionary algorithms using eleven different performance metrics for val-idation. Then, using the multiple-criteria decision-making approach, the meta-heuristics algorithms are evaluated. The obtained results demonstrate the proposed solution method algorithm's competitiveness and superiority over the other three evolutionary algorithms.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Transportation Time dependent vehicle routing problem Hazardous material Interval type-2 fuzzy logic controller multi -objective evolutionary algorithm based on decomposition Dispersion metric

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A novel evolutionary algorithm for structural model updating with a hybrid initialization and multi-stage update strategy

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OCEAN ENGINEERING 2024年 305卷

作者： Jiang, Yufeng Ma, Chunke Wang, Shuqing Li, Yunhui Ocean Univ China Coll Engn 238 Songling Rd Qingdao City 266100 Peoples R China

Measurement errors, spatial incompleteness, and parameter uncertainties frequently lead to significant deviations between the estimated correction coefficients of structural model updating and their actual values. To address the limitations, a novel multi-objective evolutionary algorithm called MOEA-HiMs is proposed for structural model updating, which incorporates a hybrid initialization and multi-stage update strategy. Two main contributions are summarized: (a) the introduction of optimal point and elite set initialization to enhance population diversity, which is achieved through the use of Latin hypercube sampling and a regularization technique, respectively;(b) the division of the optimization process into multiple stages, where solutions are corrected to improve converging efficiency using an appropriate update strategy. Additionally, the elite set is selected through the execution of sensitivity classification using a novel mathematical sensitivity index. The effectiveness and robustness of the MOEA-HiMs are demonstrated by the numerical offshore jacket platform and an experimentally-scaled platform structure. Both numerical and experimental results prove that the novel proposed MOEA-HiMs is more effective and robust than the traditional MOEA when using limited information, even under heavy noise conditions. The algorithm can serve as preliminary methods for online structural model updating of offshore platforms.

关键词： Structural model updating Offshore platform Sensitivity classification multi -objective evolutionary algorithm Regularization technique Hybrid strategy

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post-MORDM: Mapping policies to synthesize optimization and robustness results for decision-maker compromise

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ENVIRONMENTAL MODELLING & SOFTWARE 2022年 157卷

作者： Bonham, Nathan Kasprzyk, Joseph Zagona, Edith Univ Colorado Boulder Civil Environm & Architectural Engn Dept Boulder CO 80309 USA Univ Colorado Boulder Ctr Adv Decis Support Water & Environm Syst Boulder CO 80309 USA

This paper introduces post-MORDM, a decision-support framework that augments Many objective Robust De-cision Making (MORDM). MORDM often creates an intractable number of environmental management policies, characterized by decision variable, objective, and robustness values. This large number of policies inhibits de-cision support, causing disagreements among decision-makers. Post-MORDM addresses these challenges via the Self-Organizing Map (SOM), synthesizing MORDM data as layers organized in a map-like coordinate system. It uses the SOM to cluster policies, discover salient characteristics, and assess cause-effect relationships between decision-maker choices (i.e. decision variable values) and performance (objective and robustness values). Overall, the goal of post-MORDM is to create a structured platform that encourages negotiation and compromise. We demonstrate post-MORDM with a case study of two illustrative decision-makers for reservoir operation policy in the Colorado River Basin, USA. Post-MORDM helps communicate tradeoffs between storage and delivery objectives, relate tradeoffs to shortage policies, and identify mutually feasible policies.

关键词： Self -organizing maps (SOM) Decision -support Robustness Tradeoff analysis Colorado River Basin multi -objective evolutionary algorithm (MOEA)

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