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Decomposition-Based multiobjective evolutionary Optimization With Tabu Search for Dynamic Pickup and Delivery Problems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第10期25卷 14830-14843页

作者： Cai, Junchuang Zhu, Qingling Lin, Qiuzhen Ming, Zhong Tan, Kay Chen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Dynamic pickup and delivery problems (DPDPs) with various constraints, such as docks, time windows, capacity, and last-in-first-out loading, have posed significant challenges for existing vehicle routing algorithms, as most of them only optimize a single weighted objective function, which makes it difficult to maintain the solutions' diversity and may easily become stuck in local optima. To alleviate this issue, this paper introduces a decomposition-based multiobjective evolutionary algorithm with tabu search for solving the above DPDPs. First, our algorithm leverages multiobjectivization and reformulates the DPDP as a multiobjective optimization problem (MOP), which is further decomposed into multiple subproblems. Then, these subproblems are approached simultaneously and collaboratively by using a crossover process to enhance the diversity of the solutions, followed by using an efficient tabu search to speed up the convergence. In this way, our algorithm can better balance the trade-off between exploration and exploitation for solving this MOP, and then one promising solution can be selected from the population to complete some pickup and delivery tasks in an interval of the DPDP. Simulation results on 64 test problems from a practical scenario of Huawei demonstrate that the proposed algorithm outperforms other competitive algorithms for tackling DPDPs. Additionally, more experiments are conducted on 20 large-scale distribution problems within JD Logistics to validate the generalization capability of our algorithm.

关键词： Optimization Heuristic algorithms Vehicle dynamics Search problems Convergence Production facilities Logistics Dynamic pickup and delivery problem multiobjectivization decomposition multiobjective evolutionary algorithm tabu search combinatorial optimization

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multiobjective coverage path planning: Enabling automated inspection of complex, real-world structures

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APPLIED SOFT COMPUTING 2017年 61卷 264-282页

作者： Ellefsen, K. O. Lepikson, H. A. Albiez, J. C. SENAI CIMATEC Brazilian Inst Robot Salvador BA Brazil SENAI CIMATEC SENAI Inst Innovat Automat Salvador BA Brazil DFKI GmbH Robot Innovat Ctr Bremen Germany Univ Oslo Dept Informat Oslo Norway

An important open problem in robotic planning is the autonomous generation of 3D inspection paths that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously- and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. multiobjective evolution, by optimizing energy usage and coverage together, ensures a good balance between the two-both when 100% coverage is feasible, and when large parts of the object are hidden. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Coverage path planning multiobjective evolutionary algorithm Robotic inspection

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A multi-clustering method based on evolutionary multiobjective optimization with grid decomposition

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SWARM AND evolutionary COMPUTATION 2020年 55卷 100691-100691页

作者： Wang, Lisong Cui, Guonan Zhou, Qing Li, Kui Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China China Aeronaut Radio Elect Res Inst Natl Key Lab Sci & Technol Avion Integrat Shanghai 200233 Peoples R China

At present, it is a challenging task to determine the number of clusters (k), which has a great impact on the quality of major clustering methods. Multi-objective evolutionary algorithms (MOEAs), which determines k value adaptively, have been widely adopted for clustering. However, when the range of k becomes increasingly large, Pareto Front (PF) approximations obtained by an MOEA may not be uniformly distributed, leading to the difficulty of obtaining the optimal k value for clustering. For this reason, an MOEA base on constrained decomposition with grids (CCDG-K) is designed for clustering and better identify the optimal k value. CCDG-K adopts a grid system for decomposition. The grid system has an inherent property of reserving diversely populated solutions, which is very desirable for clustering. The experimental studies show that CCDG-K can deliver solutions of all k values which is of great help for obtaining the optimal k value. The experimental results also indicate that CCDG-K outperforms other algorithms in clustering.

关键词： Clustering Constrained decomposition with grids Pareto front multiobjective evolutionary algorithm

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NSGA and SPEA applied to multiobjective design of power distribution systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2006年第4期21卷 1938-1945页

作者： Mendoza, Franklin Bernal-Agustin, Jose L. Dominguez-Navarro, Jose A. Univ Nacl Expt Politecn Antonio Jose de Sucre Dept Elect Engn Puerto Ordaz Venezuela Univ Zaragoza Dept Elect Engn Zaragoza Spain

This paper presents, for the first time, an application of two well-know multiobjective optimization techniques, namely, nondominated sorting genetic algorithm (NSGA) and strength Pareto evolutionary algorithm (SPEA), to the multiobjective design of power distribution systems. These algorithms have been applied to a multiobjective optimization problem with some technical constraints, minimizing the total costs while maximizing the reliability of the power distribution system. The NSGA uses a fitness sharing scheme to achieve diversity among the obtained solutions. In SPEA, it is necessary to apply a reduction procedure because of the number of solutions. For this purpose, a fuzzy c-means (FCM) clustering algorithm has been applied, with this being the first time that an FCM algorithm in the SPEA has been used. The obtained results from both techniques have been compared, concluding that both offer similar efficiency in order to solve the stated multiobjective optimization problem. The developed methodology is applicable to practical cases of design, allowing for additional requirements that the designer imposes.

关键词： fuzzy c-means (FCM) clustering multiobjective evolutionary algorithm power distribution system design

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Multitask Computation Offloading Based on evolutionary multiobjective Optimization in Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 15894-15908页

作者： Chai, Zheng-Yi Zhao, Ying-Jie Li, Ya-Lun Tiangong Univ Sch Comp Sci & Technol Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

The development of Industrial Internet of Things (IIoT) has completely changed traditional manufacturing industry. Industrial equipments with limited resources often cannot meet the diverse demands of numerous computing-intensive and latency-sensitive tasks. Mobile edge computing (MEC) offloads these tasks to nearby edge servers to achieve lower latency and energy consumption. However, considering the channel interferences of the network and the diverse demands of different tasks, coordinating computation offloading among multiple devices is challenging. To address this challenge, the computation offloading is formulated as a multiobjective optimization problem, and a new task model composed of scientific workflow tasks and concurrency workflow tasks is proposed to represent the multitask in the industrial environment. In addition, a two-hierarchical optimization framework is devised to optimize the bandwidth allocation and the multitask computation offloading through the dynamic bandwidth preallocation and the improved multiobjective evolutionary algorithm based on decomposition with two performance enhancing schemes. Comprehensive experiments demonstrate that the effectiveness and efficiency of our proposed framework in terms of the tradeoffs between latency and energy consumption, as well as the convergence and diversity of obtained nondominated solutions.

关键词： Task analysis Energy consumption Optimization Industrial Internet of Things Servers Computational modeling evolutionary computation Computation offloading Industrial Internet of Things (IIoT) mobile edge computing (MEC) multiobjective evolutionary algorithm multitask offloading

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Adaptive decomposition-based evolutionary approach for multiobjective sparse reconstruction

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INFORMATION SCIENCES 2018年 462卷 141-159页

作者： Yan, Bai Zhao, Qi Wang, Zhihai Zhang, J. Andrew Beijing Univ Technol Inst Laser Engn Beijing 100124 Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Beijing Univ Technol Key Lab Optoelect Technol Minist Educ Beijing 100124 Peoples R China Univ Technol Sydney GBDTC Sydney NSW 2007 Australia

This paper aims at solving the sparse reconstruction (SR) problem via a multiobjective evolutionary algorithm. Existing multiobjective evolutionary algorithms for the SR problem have high computational complexity, especially in high-dimensional reconstruction scenarios. Furthermore, these algorithms focus on estimating the whole Pareto front rather than the knee region, thus leading to limited diversity of solutions in knee region and waste of computational effort. To tackle these issues, this paper proposes an adaptive decomposition-based evolutionary approach (ADEA) for the SR problem. Firstly, we employ the decomposition-based evolutionary paradigm to guarantee a high computational efficiency and diversity of solutions in the whole objective space. Then, we propose a two stage iterative soft-thresholding (IST)-based local search operator to improve the convergence. Finally, we develop an adaptive decomposition -based environmental selection strategy, by which the decomposition in the knee region can be adjusted dynamically. This strategy enables to focus the selection effort on the knee region and achieves low computational complexity. Experimental results on simulated signals, benchmark signals and images demonstrate the superiority of ADEA in terms of reconstruction accuracy and computational efficiency, compared to five state-of-the-art algorithms. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Sparse reconstruction multiobjective evolutionary algorithm Adaptive decomposition Reference vector

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Exploratory study of sorting particle swarm optimizer for multiobjective design optimization

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MATHEMATICAL AND COMPUTER MODELLING 2010年第11-12期52卷 1966-1975页

作者： Feng, Yixiong Zheng, Bing Li, Zhongkai Zhejiang Univ Engn & Comp Graph Inst Dept Mech Engn State Key Lab Fluid Power Transmiss & Control Hangzhou 310027 Zhejiang Peoples R China Zhejiang Gongshang Univ Coll Business Adm Hangzhou 310018 Zhejiang Peoples R China China Univ Min & Technol Coll Mech & Elect Engn Xuzhou 221116 Peoples R China

To solve problems such as low global search capability and insufficient diversity of Pareto optimal set existing in MOPSO, a multiobjective particle swarm optimization algorithm based on crowding distance sorting is proposed. An external population is preserved to store the non-dominated individuals during the evolution process. The shrink of the external population is achieved based on individuals' crowding distance sorting by descending order, which deletes the redundant individuals in the crowding area. An individual with relatively big crowding distance is selected as the global best to lead the particles evolving to the disperse region. The dominant relation between individuals is compared with the constraint Pareto dominance to embody the constraints without external parameters. The experiments of six standard unconstrained test problems illustrate that the new algorithm is competitive with NSGA-II and SPEA2 in terms of converging to the true Pareto front and maintaining the diversity of the population. The effectiveness of the algorithm for constraint problems is proved by solving three constraint test problems. Moreover, the best value ranges of mutation rate and inertia weight are analyzed by numerical experiments to guarantee the steady convergence of the algorithm. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： multiobjective particle swarm optimization multiobjective evolutionary algorithm Crowding distance Pareto optimal set Standard test problems

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multiobjective evolutionary optimization for feature-based simplification of 3D boundary representation models

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第9-10期110卷 2603-2618页

作者： Kwon, Soonjo Kim, Hyungki Mun, Duhwan Kumoh Natl Inst Technol Dept Mech Syst Engn Gumi South Korea Chonbuk Natl Univ Div Comp Sci & Engn Jeonju South Korea Korea Univ Sch Mech Engn Seoul South Korea

Boundary representation (B-rep) is one of the most common techniques for representing three-dimensional (3D) shapes. In this method, shapes are described mathematically based on the vertices, edges, and faces present. B-rep models are generated using computer-aided design systems and are employed for various purposes in downstream applications, including computer-aided engineering, manufacturing, and inspection. Therefore, the level of detail of the 3D model should be adjusted based on the purpose of use. In this study, we performed multiobjective optimization for the feature-based simplification of B-rep models. The two objectives of the general model simplification process are to minimize the data size and limit the differences in appearance with respect to the original model. To simultaneously meet these objectives, model simplification was performed to minimize both the difference in the volume and the number of faces with respect to those of the original model. For optimization, we used the simple genetic algorithm (SGA) and the non-dominated sorting genetic algorithm II (NSGA-II). The experimental results confirmed that solution set obtained using NSGA-II was of higher quality as compared with those for SGA and the conventional method.

关键词： Model simplification B-rep model multiobjective optimization multiobjective evolutionary algorithm Genetic algorithm NSGA-II

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multiobjective evolutionary optimization of substation maintenance using decision-varying Markov model

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IEEE TRANSACTIONS ON POWER SYSTEMS 2008年第3期23卷 1328-1335页

作者： Yang, F. Kwan, Chung Min Chang, C. S. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

Reducing overall substation cost and improving reliability are the two prime but often conflicting objectives of electric power distribution. Proper scheduling of substation preventive maintenance provides an effective means to tradeoff between these two objectives. Decision-varying Markov models relating the deterioration process with maintenance operations is proposed to predict the availability of individual component. Minimum cut-sets method is employed to identify the critical components and evaluate the overall reliability of substation. A multiobjective evolutionary algorithm is proposed to optimize the two objectives to provide Pareto-fronts or tradeoff curves for a holistic view of the conflicting relationships between them. Through simulations, abilities of our proposed algorithm are demonstrated for robust search towards optimal solutions for large-size distribution.

关键词： decision-varying Markov model minimum cut sets multiobjective evolutionary algorithm pareto front substation maintenance

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multiobjective evolutionary Optimization of Training and Topology of Recurrent Neural Networks for Time-Series Prediction

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COMPUTER JOURNAL 2012年第3期55卷 325-336页

作者： Katagiri, Hideki Nishizaki, Ichiro Hayashida, Tomohiro Kadoma, Takanori Hiroshima Univ Fac Engn Higashihiroshima Hiroshima 7398527 Japan

This paper provides a new evolutionary multiobjective optimization method for automatically optimizing the network topology of recurrent neural networks (NNs). To obtain NNs with higher prediction capability for time-series data, the proposed method is constructed by focusing on the intensively exploration of a feasible region including solutions with small training errors on the Pareto frontier, unlike existing evolutionary multiobjective optimization methods, which aim to find a whole set of the Pareto optimal solutions. Our method is characterized by the ideas of self-adaptive mutation probability setting, elite preservation strategies and archive for the preservation of local optimal solutions. Through the comparison with the performances of the most promising existing method by Delgado et al. using benchmark time-series data instances, it is shown that the proposed method is superior to the existing effective algorithm with respect to the capability of time-series prediction.

关键词： recurrent neural networks time-series prediction multiobjective evolutionary algorithm

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