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PEAC-HNF: A Novel Multi-Objective evolutionary Algorithm for Split Delivery Vehicle Routing With Three-Dimensional Loading Constraints

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2024年

作者： Zhang, Han Li, Qing Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518000 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Lingnan Univ Sch Data Sci Hong Kong Peoples R China

The Split Delivery Vehicle Routing Problem with Three-Dimensional Loading Constraints (3L-SDVRP) integrates routing and packing problems, aiming to maximize the vehicle load efficiency and minimize the total travel distance. Solving 3L-SDVRP is critical for logistics and transportation industries. However, achieving an appropriate balance between exploration (searching for new solutions) and exploitation (refining known solutions) in metaheuristic algorithms for 3L-SDVRP, especially under limited computational resources, remains challenging. Furthermore, the application of multi-objective optimization algorithms to the 3L-SDVRP remains a largely unexplored area, particularly when considering the inherent trade-offs between the two conflicting objectives. To address these challenges, this paper introduces a new Pareto-based evolutionary Algorithm with Concurrent crossover and Hierarchical Neighborhood Filtering mutation (PEAC-HNF), distinguished by its novel Hierarchical Neighborhood Filtering (HNF) mutation. The HNF mutation uses diverse neighborhood structures to generate offspring, adopts a hierarchical strategy prioritizing individuals with higher nondomination ranks, and incorporates an offspring filtering process to save computational resources. HNF allows PEAC-HNF to improve its exploitation capabilities while maintaining exploration strengths, achieving a balanced performance. Comparisons with state-of-the-art algorithms across various problem instances (242 instances in total) demonstrate the effectiveness of PEAC-HNF. Further analysis highlights the critical role of the HNF mutation in enhancing algorithmic performance. The utilization of the HNF mutation can extend beyond PEAC-HNF to other complex optimization problems.

关键词： Loading Vehicle routing Three-dimensional displays Search problems Metaheuristics evolutionary computation Routing Mathematical models Indexes Complexity theory three-dimensional packing multi-objective optimisation metaheuristic algorithms evolutionary algorithms

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Evaluating Meta-Heuristic Algorithms for Dynamic Capacitated Arc Routing Problems Based on a Novel Lower Bound Method

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IEEE computationAL INTELLIGENCE MAGAZINE 2024年第4期19卷 31-44页

作者： Tong, Hao Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin Univ Birmingham Birmingham England Honda Res Inst Europe GmbH Offenbach Germany Lingnan Univ Hong Kong Peoples R China

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the true quality of the solutions found because the true optima are unknown, especially in dynamic environments. It would be desirable to evaluate algorithms not only relatively through comparisons with others, but also in absolute terms by estimating their quality compared to the true global optima. Unfortunately, true global optima are normally unknown or hard to find since the problems being addressed are NP-hard. In this paper, instead of using true global optima, lower bounds are derived to carry out an objective evaluation of the solution quality. In particular, the first approach capable of deriving a lower bound for dynamic capacitated arc routing problem (DCARP) instances is proposed, and two optimization algorithms for DCARP are evaluated based on such a lower bound approach. An effective graph pruning strategy is introduced to reduce the time complexity of our proposed approach. Our experiments demonstrate that our approach provides tight lower bounds for small DCARP instances. Two optimization algorithms are evaluated on a set of DCARP instances through the derived lower bounds in our experimental studies, and the results reveal that the algorithms still have room for improvement for large complex instances.

关键词： Heuristic algorithms Metaheuristics Avalanche photodiodes evolutionary computation Routing Approximation algorithms Vehicle dynamics Time complexity Performance evaluation Benchmark testing Optimization methods

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Comparative Review of Multi-Objective Optimization Algorithms for Design and Safety Optimization in Electric Vehicles

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IEEE ACCESS 2024年 12卷 146376-146396页

作者： Dharma, I. Gede S. S. Setiawan, Rachman Inst Teknol Bandung Fac Mech & Aerosp Engn Doctoral Program Mech Engn Bandung 40132 West Java Indonesia Inst Teknol Bandung Fac Mech & Aerosp Engn Bandung 40132 West Java Indonesia

Despite the widespread use of established optimization algorithms like Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and Multi-Objective evolutionary Algorithm based on Decomposition (MOEA/D) in real-world engineering optimization problems, newer algorithms such as Two-Stage NSGA-II (TS-NSGA-II), Dynamic Constrained NSGA-III (DCNSGA-III), MOEA/D with Virtual Objective Vectors (MOEA/D-VOV), Large-Scale evolutionary Multi-Objective Optimization Assisted by Directed Sampling (LMOEA-DS), and Super-Large-Scale Multi-Objective evolutionary Algorithm (SLMEA) remain underexplored in the context of Battery Electric Vehicle (BEV) safety, particularly in optimizing complex, non-linear, and constrained multi-objective problems like crashworthiness and thermal management. This study addresses this gap by comparing these newer algorithms against traditional methods using a newly introduced benchmark problem focused on BEV battery protection (RWMOP-BEV). The design problem aimed to maximize energy absorption during impact, enhance crash force efficiency, and optimize temperature difference, all while adhering to design space and operational constraints. The comparative results, based on four performance indicators-hypervolume (HV), inverted generational distance (IGD), averaged Hausdorff distance (Delta(p)) , and spread-reveal that SLMEA emerged as the best algorithm, not only for RWMOP-BEV but also across other benchmark sets, including DTLZ problems and other real-world multi-objective optimization problems.

关键词： Batteries Optimization Accidents Benchmark testing Thermal management Electric vehicles Heuristic algorithms Measurement Heating systems evolutionary computation Multi-objective optimization algorithm battery electric vehicle (BEV) safety crashworthiness thermal management

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Flexible Job Shop Scheduling Method for Optimizing Mold Resource Setup Time

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IEEE ACCESS 2024年 12卷 33486-33503页

作者： Cheng, Yi Xie, Zhijun Xin, Yu Chen, Kewei Zarei, Roozbeh Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Ningbo Univ Fac Mech Engn Ningbo 315211 Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia

In a practical development environment, completing a job often necessitates the coordinated allocation of multiple resources. However, resource constraints such as molds are rarely considered in the multi-resource job shop scheduling problem. These molds have sequence-dependent setup times and are fully occupied during processing. Therefore, this study focuses on the Flexible Job Shop Scheduling Problem with Mold Resource Setup Time Optimization (FJSP-MRST), which aims to minimize setup time and makespan. Considering the characteristics of mold setup time, this paper introduces a Mold-Machine Double Fusion Insertion Method to optimize the mold-machine usage order and improve the mold change time calculation method. This innovative approach improves the overall scheduling process by efficiently managing the utilization of molds and machines. Furthermore, we propose a Mold-Operation Layer Loading Method to compress setup time, offering a novel strategy for reducing the setup time for mold resources. These two pioneering methods are integrated with an enhanced multi-objective differential evolution algorithm to obtain the optimal solution for FJSP-MRST. Experimental results demonstrate that the proposed methods can render the arrangement of machines and molds under the current process sequence more reasonable and effectively eliminate implicit setup time.

关键词： Job shop scheduling Biological cells Optimization Machining Loading Codes Sorting evolutionary computation Resource management Optimization methods Fusion power generation Flexible job shop scheduling problem mold resource setup time differential evolution algorithm

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An Optimal Unsupervised Domain Adaptation Approach With Applications to Pipeline Fault Diagnosis: Balancing Invariance and Variance

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第8期20卷 10019-10030页

作者： Wang, Chuang Wang, Zidong Liu, Hongjian Dong, Hongli Lu, Guoping Northeast Petr Univ Sanya Offshore Oil & Gas Res Inst Sanya 572025 Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligent Daqing 163318 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH England Anhui Polytech Univ Key Lab Adv Percept & Intelligent Control High end Minist Educ Wuhu 241000 Peoples R China Anhui Polytech Univ Sch Math & Phys Wuhu 241000 Peoples R China Nantong Univ Sch Elect Engn Nantong 226019 Peoples R China

A practical yet challenging scenario in transfer learning is unsupervised domain adaptation (UDA), where knowledge is transferred from a labeled source domain to unlabeled target domains. The crucially important role of domain-variant characteristics is often neglected by most existing UDA methods, which can deteriorate adaptation performance and result in negative transfer. In this article, an optimal unsupervised domain adaptation (OUDA) algorithm is proposed in order to address this issue, which balances the invariance of domain-sharing features and the variance of domain-specific features. In the proposed approach, a gradient adversarial adaptation (GAA) method is introduced to align the gradient directions of source and target features within the same category, thereby facilitating knowledge transfer. In addition, a local manifold embedding (LME) technique is proposed to preserve the intrinsic geometric structure of the original feature space while implementing distribution alignment, providing distinguishable features for UDA. To stabilize the process of knowledge transfer, an evolutionary control strategy is developed to adaptively control the tradeoff between the GAA and LME by employing the particle swarm optimization algorithm. Extensive experiments are conducted on cross-domain natural gas pipeline fault diagnosis, and the results on nine cross-domain classification tasks indicate that our OUDA algorithm outperforms the existing state-of-the-art UDA methods. Moreover, the performance analysis in terms of accuracy, loss, and domain divergence demonstrates the superior stability of the proposed OUDA algorithm in dealing with unsupervised knowledge transfer.

关键词： Manifold learning Feature extraction Fault diagnosis Measurement Knowledge transfer Training Pipelines evolutionary computation invariance and variance manifold learning unsupervised domain adaptation (UDA)

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Radioactivity estimation of multiple radiation sources using a Compton camera to investigate radioactively contaminated objects

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APPLIED RADIATION AND ISOTOPES 2024年 203卷 111083页

作者： Sato, Yuki Japan Atom Energy Agcy Collaborat Labs Adv Decommissioning Sci 790-1 Motooka Aza OhtsukaTomioka Machi Fukushima Japan

Radioactive substances released during the accident at the Fukushima Daiichi Nuclear Power Station (FDNPS) were deposited on various equipment and building structures. During decommissioning, an investigation of the radioactive substances deposited inside the contaminated equipment and structures can provide information regarding the cause and progression of the accident. This study introduces a quantitative evaluation method for the amount of radioactivity. In this method, a Compton camera, a type of gamma-ray imager, is used to investigate the deposition and contamination level of radioactive substances on contaminated objects. Multiple 137Cs radiation sources with different radioactivities were placed horizontally in one dimension within the field of view of the Compton camera, and a proof-of-principle study was conducted to evaluate the amount of radioactivity of each source quantitatively.

关键词： Gamma-ray imaging Compton camera Radioactivity estimation Multiple radiation sources evolutionary computation

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Transfer-Based Particle Swarm Optimization for Large-Scale Dynamic Optimization With Changing Variable Interactions

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IEEE TRANSACTIONS ON evolutionary computation 2024年第6期28卷 1633-1643页

作者： Liu, Xiao-Fang Zhan, Zhi-Hui Zhang, Jun Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Hanyang Univ ERICA Ansan 15588 South Korea

Cooperative coevolutionary algorithms are popular to solve large-scale dynamic optimization problems via divide-and-conquer mechanisms. Their performance depends on how decision variables are grouped and how changing optima are tracked. However, existing decomposition methods are computationally expensive, resulting in limitations under dynamic variable interactions. Quick online decomposition is still a challenging issue, along with solution reconstruction for new subproblems. This article proposes transfer-based particle swarm optimization, which adopts a dynamic differential grouping (DDG) for online decomposition and a solution transfer strategy in response to environmental changes. Particularly, once an environmental change occurs, the DDG readjusts historical groupings based on the change severity of variable interactions. In addition, according to the similarity between subproblems in successive environments, the solution transfer strategy constructs new solutions from historical ones through dimension mapping. Multiple swarms are created to explore subareas of subproblems. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms on problem instances up to 1000-D in terms of solution optimality. The DDG obtains accurate groupings using less function evaluations.

关键词： Heuristic algorithms Optimization Statistics Sociology Manuals Upper bound Cooperative coevolution decomposition dimension mapping evolutionary computation particle swarm optimization (PSO) transfer learning transfer learning

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Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

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IEEE ACCESS 2024年 12卷 22991-23028页

作者： Makhadmeh, Sharif Naser Al-Betar, Mohammed Azmi Abu Doush, Iyad Awadallah, Mohammed A. Kassaymeh, Sofian Mirjalili, Seyedali Abu Zitar, Raed Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Univ Petra Dept Data Sci & Artificial Intelligence Amman 11196 Jordan Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid 50 Jordan Amer Univ Kuwait Coll Engn & Appl Sci Dept Comp Salmiya 13034 Kuwait Yarmouk Univ Dept Comp Sci Irbid 21163 Jordan Al Aqsa Univ Dept Comp Sci Gaza 4051 Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Aqaba Univ Technol Fac Informat Technol Dept Software Engn Aqaba 77110 Jordan Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi U Arab Emirates

The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.

关键词： Grey Wolf Optimizer swarm intelligence optimization evolutionary computation

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Multi-objective multi-period optimal site and size of distributed generation along with network reconfiguration

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IET RENEWABLE POWER GENERATION 2024年第16期18卷 3704-3730页

作者： Abbas, Ghulam Wu, Zhi Ali, Aamir Southeast Univ Sch Elect Engn Nanjing Peoples R China Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah Sindh Pakistan

While extensive research has focused on enhancing distribution networks through either maximizing Distributed Generation (DG) integration or network reconfiguration at specific times, there is a need for further investigation into concurrently optimal network reconfiguration and DG allocation. To reduce the cost of energy delivered, the cost of energy loss, and voltage deviation, this study gives a dynamic multi-objective network reconfiguration together with siting and sizing of dispatchable and non-dispatchable DGs. The widely used IEEE 33-bus and a large-scale 118-bus radial test system are employed while considering the time sequence fluctuations in sunlight irradiation and load. To address the pointed-out challenge of multiperiod optimal DG allocation and reconfiguration while simultaneously decreasing the cost of energy supplied, the cost of energy lost, and the voltage deviation, a novel Multi-objective Bidirectional co-evolutionary algorithm (BiCo) is implemented. For better exploration and exploitation, the proposed algorithm integrating the constraint domination principle evolves the population from the feasible and infeasible search space with the help of a novel angle-based density section. Simulation results demonstrate that the proposed approach outperforms previously published Multi-objective evolutionary Algorithms (MOEAs) by discovering a vast collection of uniformly spaced non-dominated solutions in a single simulation run. Further, a fuzzy set theory is applied to find the best compromise solution among obtained final non-dominated solutions. The results establish that the Pareto solutions significantly improved the system's voltage profile, with savings of over 22% compared to the baseline case and an exceptional improvement of over 80% in voltage deviation and power loss. The base configuration of the proposed 118-bus network. image

关键词： distribution network evolutionary computation optimisation renewable energy sources

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Optimization of an Air Pressure System: A Multi-Objective Control and Modeling Approach

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IEEE ACCESS 2024年 12卷 96691-96703页

作者： Huilcapi, Victor Castillo, Christian Sanchez, Daniel Cajo, Ricardo Univ Politecn Salesiana Fac Ingn Guayaquil 090109 Ecuador Escuela Super Politecn Litoral Fac Ingn Elect & Comp ESPOL Campus Gustavo Galindo Guayaquil 9000 Ecuador

This paper presents the application of a methodological approach based on multi-objective optimization for identifying a nonlinear model and designing controllers for an air pressure system. First, the parameters of the system's nonlinear model are determined based on experimental data obtained from the process. Subsequently, the methodology is extended to tuning cascade PI controllers, where experimental tests are performed in the laboratory. This methodology allows a designer to have a detailed view of the proposed multi-objective problem (both in the modeling and controlling the air pressure system). Depending on the level of detail required, the design objectives of the multi-objective problem are proposed. In this case, two objectives are proposed to evaluate the model's performance and three objectives for evaluating the control system's quality. Among the advantages offered by this methodology is the easy understanding of the conflicts that arise between the design objectives, which allows for selecting an optimal solution according to the preferences established by a designer and based on detailed information on the system's performance. In this case, a conflict between the dynamics of the pressures of the air pressure system could be evidenced, both in the modeling and in the control. The proposed methodology also allows the analysis of the behavior of the air pressure system in a global framework without loss of information, which would not be feasible in a traditional single-objective analysis that agglutinates all the information in a single design objective. Although the decision-making stage of the proposed methodology is more complex than a single-objective analysis, it is justified as it provides detailed and complete information to a designer to make accurate and effective decisions on how to model and control the compressed air pressure system effectively.

关键词： Optimization methods Atmospheric modeling Tuning Vectors evolutionary computation Visualization PI control Pareto optimization Pressure measurement Air quality Air pressure systems multi-objective optimization methodology multi-objective evolutionary algorithm (ev-MOGA) level diagram (LD) cascade PI controller nonlinear model decision-making processes

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