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multi-objective Optimization for multimodal multi-objective multi-Point Shortest Path Problem Considering Unforeseeable Road Eventualities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Xu, Zhiwei Zhang, Kai Del Ser, Javier Li, Miqing Xu, Xin He, Juanjuan Wu, Ni Wuhan Univ Sci & Technol Sch Comp Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc andRe Wuhan 430065 Hubei Peoples R China Tecnalia Basque Res & Technol Alliance BRTA Bizkaia 48160 Bilbao Spain Univ Basque Country Dept Commun Engn UPV EHU Bizkaia 48013 Bilbao Spain Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

multi-objective multi-point shortest path planning problems are commonly encountered in real-world applications. Numerous path planning algorithms have been proposed to accommodate different model assumptions. However, most existing algorithms can only identify a subset of the Pareto optimal paths and overlook equivalent Pareto optimal paths. Relying solely on a subset of Pareto optimal solutions is insufficient to effectively respond to unforeseeable road eventualities in the real-world traffic environment. In this paper, multi-objective multi-point shortest path planning problem is modeled as a multimodal multi-objective optimization problem with necessary points constrains. A multimodal multi-objective evolutionary algorithm using constraint dominance principle-based path comparison strategy and path similarity-based multimodal solutions selection strategy is proposed to address this problem. The proposed constraint dominance principle-based path comparison strategy can effectively navigate through large infeasible regions by relaxing necessary point constraints, thereby obtaining a true constrained Pareto front. The proposed path similarity-based multimodal solutions selection strategy can effectively balance the distribution of solutions in the decision space, thereby preserving multiple equivalent optimal solutions. The proposed algorithm is compared with five state-of-the-art path planning algorithms from the benchmark test suite derived from the 2021 IEEE CEC path planning competition, where city maps are adapted from real transportation networks in Chinese cities, in our experiments. The exceptional performance is demonstrated through thirty independent runs, yielding experimental results that showcase the superiority of the proposed algorithm on the test problem set. This superior performance highlights the potential for designing more resilient path planners suitable for scenarios affected by unpredictable road eventualities.

关键词： Path planning Optimization Classification algorithms Shortest path problem Pareto optimization Search problems Heuristic algorithms Roads evolutionary computation Convergence multi-objective shortest path planning constrained multi-objective optimization multimodal multi-objective optimization multi-objective evolutionary algorithm

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A nondominated sorting simplified swarm optimization with local search mechanisms for multi-objective vehicle routing problems with time windows

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Lai, Chyh-Ming Chiu, Chun-Chih Chen, Tzu-Li Natl Def Univ Grad Sch Resources Management & Decis Sci Taipei 112 Taiwan Natl Taipei Univ Technol Dept Business Management Taipei 106 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan

In addressing the complexities of modern logistics, this study introduces a novel multi-objective formulation for vehicle routing problems with time windows (MO-VRPTW), targeting minimizing travel distance, enhancing customer satisfaction, and equalizing driver workloads. We introduce an innovative hybrid multi-objective evolutionary algorithm (MOEA) leveraging nondominated sorting simplified swarm optimization to effectively merge the advantages of various optimization strategies. A key aspect of this advancement is the incorporation of the Lin- KernighanHelsgaun (LKH) heuristic, which delivers a superior initial solution, thereby markedly enhancing the speed of convergence. Additionally, we pioneered a local search method inspired by the A* algorithm designed to refine the search process's exploration and exploitation stages. Solomon's benchmark instances, a recognized standard in the VRPTW field, were used to validate our algorithm's effectiveness. Our algorithm demonstrated superior performance in addressing MO-VRPTW through meticulous statistical analysis, outperforming state-of-the-art algorithms, such as MOPSO, NSGA-II, MOEA/D, and SPEA2, regarding efficiency and solution diversity. This study not only advances algorithmic performance but also thoughtfully considers the interests of key supply chain stakeholders.

关键词： Vehicle routing problems with time windows multi-objective evolutionary algorithm Lin-Kernighan heuristic A* algorithm MOPSO NSGA-II MOEA/D SPEA2 Nondominated sorting simplified swarm optimization

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Gradient-based hybrid method for multi-objective optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Yang, Dewei Fan, Qinwei Xian Polytech Univ Sch Sci Xian 710048 Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Peoples R China

How to strike a tricky balance between convergence and diversity is still an ever-present challenge in the field of multi-objective optimization. In this paper, a hybrid method of gradient-based and improved non-dominated sorting genetic algorithm is proposed to solve this complex problem (HMGB). Initially, we propose a partition clustering method under a new criterion to divide the individuals in the target space, which not only facilitates the construction of Pareto descent directions but also prevents the population from falling into local optima. Subsequently, we improve the finite-difference method to obtain gradient information for multiple objective functions, which are used to construct Pareto descent directions that can accelerate convergence. Finally, we replaced the simulated binary crossover in NSGA-II with a normally distributed crossover, and combined it with polynomial variation to generate offspring, which we used for global exploration to increase the diversity of the population. The HMGB algorithm was compared with several state-of-the-art algorithms on benchmark functions and real-world problems. Experimental results demonstrate that the HMGB algorithm possesses strong competitiveness and effectiveness.

关键词： multi-objective optimization Descent direction Partitional clustering multi-objective evolutionary algorithm

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multi-objective flexible flow-shop rescheduling with rigid-flexible hybrid constraints and preventive maintenance

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Zhao, Ziye Chen, Xiaohui An, Youjun Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400030 Peoples R China China Three Gorges Univ Coll Mech & Power Engn Yichang 443002 Peoples R China

The synergy between production rescheduling and machine maintenance is critical, particularly incases where unforeseen equipment failures, not fully prevented by maintenance, might threaten the viability of the original plan. In this context, this paper explores a novel integrated optimization problem of production rescheduling and preventive maintenance in a capacity-limited flexible flow-shop (CLFFS), in which random equipment failures, hybrid rigid-flexible constraints of buffer capacity and due window are considered. Specifically, (1) an integrated optimization model is established to minimize the makespan, average flow time, earliness/tardiness penalty, machine workload extreme deviation and system instability;(2) an adaptive hybrid rescheduling strategy (AHRS) that amalgamates three classical rescheduling approaches is designed to effectively respond to random equipment failures;and (3) an improved bi-population cooperative evolutionary algorithm with an adaptive environment selection mechanism (AES-IBCEA) is developed to deal with the integrated problem. In the numerical experiments, Taguchi method is first employed to set the parameters of the proposed algorithm. Second, the effectiveness and superiority of designed operators and proposed AES-IBCEA are validated through algorithm comparison. Next, the competitiveness of the proposed AHRS is demonstrated by contrasting it with other rescheduling strategies, and the average improvement rate is up to 22.12%. Finally, a sensitivity analysis on the fault impact threshold (delta 0) and the individual selection threshold (beta) is performed, and the results reveal that beta has a significant impact on the algorithm's performance.

关键词： Flexible flow-shop rescheduling Preventive maintenance Rigid-flexible hybrid constraints Adaptive hybrid rescheduling strategy multi-objective evolutionary algorithm

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A Hybrid evolutionary algorithm with Heuristic Mutation for multi-objective Bi-clustering

A Hybrid Evolutionary Algorithm with Heuristic Mutation for ...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Bechikh, Slim Elarbi, Maha Hung, Chih-Cheng Hamdi, Sabrine Ben Said, Lamjed Kennesaw State Univ Kennesaw GA 30144 USA Anyang Normal Univ Anyang Peoples R China Univ Tunis SMART Lab ISG Campus Tunis Tunisia

ISBN: (纸本)9781728121536

Bi-clustering is one of the main tasks in data mining with several application domains. It consists in partitioning a data set based on both rows and columns simultaneously. One of the main difficulties in bi-clustering is the issue of finding the number of bi-clusters, which is usually a user-specified parameter. Recently, in 2017, a new multi-objective evolutionary clustering algorithm, called MOCK-Il, has shown its effectiveness in data clustering while automatically determining the number of clusters. Motivated by the promising results of MOCK-Il, we propose in this paper a hybrid extension of this algorithm for the case of bi-clustering. Our new algorithm, called MOBICK, uses an efficient solution encoding, an effective crossover operator, and a heuristic mutation strategy. Similarly to MOCK-II, MOBICK is able to find automatically the number of bi-clusters. The outperformance of our algorithm is shown on a set of real gene expression data sets against several existing state-of-the-art works. Moreover, to be able to compare MOBICK to MOCK-I and MOCK-Il, we have designed two basic extensions of MOCK-I and MOCK-Il for the case of bi-clustering that we named B-MOCK-I and B-MOCK-II. Again, the experimental results confirm the merits of our proposal.

关键词： Bi-clustering multi-objective evolutionary algorithm hybridization heuristic mutation reduced-length genetic encoding

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A Mixed-Factor evolutionary algorithm for multi-objective Knapsack Problem 18th

A Mixed-Factor Evolutionary Algorithm for Multi-objective Kn...

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18th International Conference on Intelligent Computing (ICIC)

作者： Du, Yanlian Feng, Zejing Shen, Yijun Hainan Univ Sch Informat & Commun Engn Haikou 570228 Hainan Peoples R China Hainan Univ State Key Lab Marine Resources Utilizat South Chi Haikou 570228 Hainan Peoples R China Hainan Univ Coll Appl Sci & Technol Danzhou 571737 Peoples R China

ISBN: (纸本)9783031138706;9783031138690

Nondominated-sorting plays an important role in multi-objective evolutionary algorithm in recent decades. However, it fails to work well when the target multi-objective problem has a complex Pareto front, brusque nondominated-sorting virtually steers by the conflicting nature of objectives, which leads to irrationality. In this paper, a novel mixed-factor evolutionary algorithm is proposed. A normalization procedure, i.e. mixed-factor, is introduced in the objective space, which links all the objectives for all the solutions of the problem to ease the conflicting nature. In the process of nondominated-sorting, the mixed factors of individual substitute the raw objectives. In order to ensure that the population are thoroughly steered through the normalized objective space, hybrid ageing operator and static hypermutation with first constructive mutation are used to guide the searching agents converge towards the true Pareto front. The algorithm proposed is operated on multi-objective knapsack problem. The effectiveness of MFEA is compared with five state-of-the-art algorithms, i.e., NSGA-II, NSGA-III, MOEA/D, SPEA2 and GrEA, in terms of five performance metrics. Simulation results demonstrate that MFEA achieves better performance.

关键词： multi-objective evolutionary algorithm Mixed-factor evolutionary algorithm multi-objective Knapsack Problem Normalization procedure

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Exploring cluster-dependent isomorphism in multi-objective evolutionary optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Zheng, Wei Sun, Jianyong Shanxi Univ Sch Comp & Informat Technol 92 Wucheng St Taiyuan 030006 Shanxi Peoples R China Xi An Jiao Tong Univ Sch Math & Stat 28 West Xianning Rd Xian 710049 Shaanxi Peoples R China

In this paper, a Two-Round learning-based algorithm for Continuous box-constrained multi-objective evolutionary optimization (TRACE) under the decomposition framework is proposed, in which the isomorphism relationship between the clustered Pareto Front and Pareto solution set is explored and anew time-varying adaptive crossover operator is developed. The learning process involves two stages. In the first stage, the K-means is applied to cluster the population of objective vectors. By exploring the property of cluster-dependent isomorphism between the objective space and the decision space, a parent individual for each individual is selected from the corresponding clusters in the decision space. The time-varying adaptive crossover operator is then used together with the classical polynomial mutation operator to generate anew solution based on the selected parent individuals. As part of the environmental selection process, the K-means is applied again to the combination of parent and offspring individuals in the objective space to assist in the selection of suitable solutions for each decomposed subspace. TRACE is compared with 11 state-of-the-art multi-objective evolutionary algorithms on totally 43 difficult problems with different characteristics. Furthermore, TRACE is compared with three promising multi-objective evolutionary algorithms for community detection in attribute networks. Extensive experiments show that TRACE significantly outperforms the compared algorithms inmost instances.

关键词： multi-objective evolutionary algorithm Clustering Decomposition Adaptive evolutionary operator

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An Adaptive Formulation-based Many-objective evolutionary algorithm for multi-Scenario Optimization in Data Enrichment

An Adaptive Formulation-based Many-Objective Evolutionary Al...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Fan, Liang Feng, Xudong Wang, Handing Xidian Univ Sch Artificial Intelligence Xian Peoples R China AECC Sichuan Gas Turbine Estab Sci & Technol Altitude Simulat Lab Mianyang Sichuan Peoples R China

ISBN: (纸本)9781728183923

In many practical applications, data enrichment can generate a large amount of accurate data to alleviate the problem of data scarcity. In order to make the fake data generated in data enrichment as close to the real data as possible, the data enriching model must be tuned to meet the loss requirements of multiple objectives in different scenarios, which makes it a multi-scenario many-objective optimization problem. However, due to the curse of the dimensionality of the scenario space and the objective space, the existing many-objective evolutionary algorithms cannot solve the problem in data enrichment well. To effectively handle this problem, we propose an adaptive formulation-based multi-objective evolutionary algorithm, where the aggregation function is used to reduce the dimension of the scenario space to one and the multiple objectives into three objectives through the adaptive formulation of the original problem. In this way, a multi-scenario many-objective problem is converted into a multi-objective problem which could be solved by existing multi-objective evolutionary algorithms. The proposed algorithm is applied to the practical data enrichment problem to solve the multi-scenario many-objective optimization problem and compared with NSGA-III. The experimental results demonstrate the remarkable superiority of the proposed algorithm over NSGA-III.

关键词： Data enrichment adaptive formulation scenario reduction multi-scenario optimization many-objective optimization multi-objective evolutionary algorithm

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Q-learning assisted multi-objective evolutionary optimization for low-carbon scheduling of open-pit mine trucks

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Huang, Yao Guo, Yinan Chen, Guoyu Wei, Hong Zhao, Xiaoxiao Yang, Shengxiang Ge, Shirong China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China China Univ Min & Technol Beijing Inner Mongolia Res Inst Ordos 017010 Peoples R China Anhui Univ Sci & Technol Sch Artificial Intelligence Huainan 232001 Peoples R China De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England

Mine trucks, as the core equipment of discontinuous open-pit mining technology, account for high transportation costs and vast quantities of greenhouse gases. In order to improve transportation efficiency and decrease carbon emissions, rationally scheduling shovel-truck pairs is a necessary issue. Previous studies give less consideration on carbon emissions of trucks that varies with road and driving conditions. To overcome the shortage, a constraint bi-objective optimization model is built for low-carbon scheduling problem of open-pit mine trucks, in which minimizing both idle time and carbon emissions of trucks are taken as the objectives. More especially, the limits on working time, traffic volume and the number of trucks are modeled as the constraints. Carbon emissions is formulated by multistage nonlinear function that takes road condition, load and driving state of trucks into account. As the problem-solver, Q-learning assisted multi-objective evolutionary algorithm is put forward. Four evolution states are defined by analyzing the improvement on feasibility and convergence of the population, and four problem-specific evolution operators are designed to meet different demands of the evolution. Q-learning-based selection strategy is proposed to select the most appropriate operator, with the purpose of improving the evolution efficiency. Experimental results on the real-world instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly.

关键词： Q-learning Scheduling Low-carbon Open-pit mine truck multi-objective evolutionary algorithm

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A New evolutionary multi-objective algorithm for Convex Hull Maximization

A New Evolutionary Multi-objective Algorithm for Convex Hull...

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IEEE Congress on evolutionary Computation (CEC)

作者： Hong, Wenjing Lu, Guanzhou Yang, Peng Wang, Yong Tang, Ke Univ Sci & Technol China Sch Comp Sci & Technol USTC Birmingham Joint Res Inst Intelligent Comput Hefei 230027 Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9781479974924

Many real-world problems often have several, usually conflicting objectives. Traditional multi-objective optimization problems (MOPs) usually search for the Pareto-optimal solutions for this predicament. A special class of MOPs, the convex hull maximization problems which prefer solutions on the convex hull, has posed a new challenge for existing approaches for solving traditional MOPs, as a solution on the Pareto front is not necessarily a good solution for convex hull maximization. In this work, the difference between traditional MOPs and the convex hull maximization problems is discussed and a new evolutionary Convex Hull Maximization algorithm (ECHMA) is proposed to solve the convex hull maximization problems. Specifically, a Convex Hull-based sorting with Convex Hull of Individual Minima (CH-CHIM-sorting) is introduced, as well as a novel selection scheme, Extreme Area Extract-based selection (EAE-selection). Experimental results show that ECHMA significantly outperforms the existing approaches for convex hull maximization and evolutionary multi-objective optimization approaches in achieving a better approximation to the convex hull more stably and with a more uniformly distributed set of solutions.

关键词： multi-objective optimization convex hull maximization multi-objective evolutionary algorithm

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