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A Collaborative Drone-Truck Delivery System With Memetic Computing Optimization

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第6期54卷 3618-3630页

作者： Zhai, Ruonan Mei, Yi Guo, Tong Du, Wenbo Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

With technological breakthroughs, drone deliveries have become increasingly popular, especially during the COVID-19 pandemic. Driven by both economical benefit and efficiency, drone-truck combined deliveries are in demand. However, it is very challenging to handle the collaboration between trucks and drones. Existing methods for truck-only routing cannot be directly applied, since their solution representations and search operators cannot consider the drone-truck collaborations effectively. In this article, we model the system as traveling salesman problem with drones (TSP-Ds), and propose a new Memetic algorithm named MATSP-D for solving it. Specifically, we design a new drone-truck solution representation and develop new crossover and local search operators under the new representation, which can modify the drone services effectively. MATSP-D conducts exploration by crossover, and exploitation by a variable neighborhood search process. The experimental results show that the proposed MATSP-D significantly outperforms the state-of-the-art algorithms for most test instances, especially the large instances with more complex collaborations between the truck and drone. Further analysis verifies the effectiveness of the newly developed local search operators in searching for better-drone-truck collaborations.

关键词： Drones Costs Search problems Collaboration Mathematical models Memetics Routing Collaborative drone-truck delivery evolutionary computation Memetic algorithm (MA) traveling salesman problem with drones (TSP-Ds)

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Genetic Programming for Explainable Manifold Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年

作者： Cravens, Ben Lensen, Andrew Maddigan, Paula Xue, Bing Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thereby enhancing the efficiency, interpretability, and scalability of data analysis. Despite their utility, current manifold learning methods often lack explicit functional mappings, which are critical for ensuring explainability in regulated and high-stakes applications. This paper introduces Genetic Programming for Explainable Manifold Learning (GP-EMaL), a novel integration of Genetic Programming (GP) and Explainable Artificial Intelligence (XAI). GP-EMaL leverages the inherently interpretable, tree-based structures of GP to generate explicit, functional mappings while directly addressing complexity challenges through innovative penalties for tree size, symmetry, and operator selection. By enabling customisable complexity metrics, GP-EMaL adapts to diverse application needs, achieving high manifold quality and significantly improved explainability. Comprehensive experiments demonstrate that GP-EMaL matches or exceeds the performance of existing approaches, producing simpler and more interpretable models. This work advances the state of explainable manifold learning, paving the way for its adoption in domains such as healthcare, environmental modelling, and financial analysis.

关键词： Complexity theory Manifold learning Measurement Optimization Genetic programming computational modeling Feature extraction High dimensional data evolutionary computation Syntactics genetic programming dimensionality reduction explainable artificial intelligence

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Engine Calibration With Surrogate-Assisted Bilevel evolutionary Algorithm

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第6期54卷 3832-3845页

作者： Yu, Xunzhao Wang, Yan Zhu, Ling Filev, Dimitar Yao, Xin Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT England Ford Motor Co Innovat & Res Ctr Dearborn MI 48124 USA Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Engine calibration problems are black-box optimization problems which are evaluation costly and most of them are constrained in the objective space. In these problems, decision variables may have different impacts on objectives and constraints, which could be detected by sensitivity analysis. Most existing surrogate-assisted evolutionary algorithms do not analyze variable sensitivity, thus, useless effort may be made on some less sensitive variables. This article proposes a surrogate-assisted bilevel evolutionary algorithm to solve a real-world engine calibration problem. Principal component analysis is performed to investigate the impact of variables on constraints and to divide decision variables into lower-level and upper-level variables. The lower-level aims at optimizing lower-level variables to make candidate solutions feasible, and the upper-level focuses on adjusting upper-level variables to optimize the objective. In addition, an ordinal-regression-based surrogate is adapted to estimate the ordinal landscape of solution feasibility. computational studies on a gasoline engine model demonstrate that our algorithm is efficient in constraint handling and also achieves a smaller fuel consumption value than other state-of-the-art calibration methods.

关键词： Optimization Engines Calibration Computer architecture evolutionary computation Petroleum Constraint handling Bilevel architecture constrained optimization engine calibration expensive optimization surrogate-assisted evolutionary algorithms (SAEAs)

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Cooperative Co-Evolution for Large-Scale Multiobjective Air Traffic Flow Management

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IEEE TRANSACTIONS ON evolutionary computation 2024年第6期28卷 1644-1658页

作者： Guo, Tong Mei, Yi Tang, Ke Du, Wenbo Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China

Air traffic flow management (ATFM) is the key driver of efficient aviation. It aims at balancing traffic demand against airspace capacity by scheduling aircraft, which is critical for air navigation service providers in delivering secure and sustainable air transport. Nowadays, the scale of scheduled aircraft grows dramatically along with the sharp increase in air traffic demand, which brings heavy pressure to efficient scheduling. Regarding safety and efficiency as two fundamental objectives of air transport, this article proposes a cooperative co-evolutionary algorithm to solve large-scale multiobjective ATFM problems. First, a new multiobjective co-evolution framework with an evolving external archive is devised, in which the subcomponents collaborate with each other via the knee solution of the archive. Second, a novel fuzzy decomposition method is specifically designed to split the large-scale ATFM problem into small-size subcomponents by utilizing the spatiotemporal correlations of aircraft. During optimization, the proposed algorithm can continuously receive feedback from the optimization process and make the decomposition more likely better suited to the problem. Third, a new contribution-based probabilistic resource allocation mechanism is developed to automatically assign the computing resources to the unbalanced subcomponents. Finally, a test suite with different scales extracted from real air traffic data is created. Extensive experimental results show that, given the same number of fitness evaluations, the proposed algorithm significantly outperforms the state-of-the-art baselines in terms of effectiveness on all the benchmark instances.

关键词： Aircraft Airports Metaheuristics Delays Aircraft navigation Air traffic control Trajectory Air traffic flow management (ATFM) evolutionary computation meta-heuristic multiobjective optimization

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Process Knowledge-Guided Autonomous evolutionary Optimization for Constrained Multiobjective Problems

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IEEE TRANSACTIONS ON evolutionary computation 2024年第1期28卷 193-207页

作者： Zuo, Mingcheng Gong, Dunwei Wang, Yan Ye, Xianming Zeng, Bo Meng, Fanlin China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Peoples R China China Univ Geosci State Key Lab High End Server & Storage Technol Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430078 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Univ Pretoria Dept Elect Elect & Comp Engn ZA-0002 Pretoria South Africa North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China Univ Manchester Alliance Manchester Business Sch Manchester M15 6PB England

Various real-world problems can be attributed to constrained multiobjective optimization problems (CMOPs). Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for CMOPs. Given this, a process knowledge-guided constrained multiobjective autonomous evolutionary optimization method is proposed. First, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving CMOPs.

关键词： Optimization Statistics Sociology evolutionary computation Space vehicles Search problems Optimization methods Autonomy constrained multiobjective optimization evolutionary optimization integrated coal mine energy system (ICMES) process knowledge

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A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification

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IEEE TRANSACTIONS ON evolutionary computation 2024年第3期28卷 718-732页

作者： Fan, Qinglan Bi, Ying Xue, Bing Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Genetic programming (GP) has achieved promising performance in image classification. However, GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications. Surrogate models can be efficiently computable approximations of expensive fitness evaluations. However, most existing surrogate methods are designed for evolutionary computation techniques with a vector-based representation consisting of numerical values, thus cannot be directly used for GP with a tree-based representation consisting of functions/operators. The variable sizes of GP trees further increase the difficulty of building the surrogate model for fitness approximations. To address these limitations, we propose a new surrogate-assisted GP approach including global and local surrogate models, which can accelerate the evolutionary learning process and achieve competitive classification performance simultaneously. The global surrogate model can assist GP in exploring the entire search space, while the local surrogate model can speed up convergence and further improve performance. Furthermore, a new surrogate training set is constructed to assist in establishing the relationship between the GP tree and its fitness, and effective surrogate models can be built accordingly. Experimental results on ten datasets of varying difficulty show that the new approach significantly reduces the computational cost of the GP-based method without sacrificing the classification accuracy. The comparisons with other state-of-the-art methods also demonstrate the effectiveness of the new approach. Further analysis reveals the significance of the global and local surrogates and the new surrogate training set on improving or maintaining the performance of the proposed approach while reducing the computational cost.

关键词： Image classification Feature extraction computational modeling Training evolutionary computation Predictive models computational efficiency Fitness evaluations genetic programming (GP) image classification surrogate models

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The Evolution of Automated Software Repair

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2025年第3期51卷 870-873页

作者： Le Goues, Claire Nguyen, Thanhvu Forrest, Stephanie Weimer, Westley Carnegie Mellon Univ Pittsburgh PA 15213 USA George Mason Univ Fairfax VA 22030 USA Arizona State Univ Tempe AZ 85287 USA Univ Michigan Ann Arbor MI 48109 USA

GenProg implemented a novel method for automatically evolving patches to repair test suite failures in legacy C programs. It combined insights from genetic programming and software engineering. Many of the original design decisions in GenProg were ultimately less important than its impact as an existence proof. In particular, it demonstrated that useful patches for non-trivial bugs and programs could be generated automatically. Since the original publication, research in automated program repair has expanded to consider and evaluate many new methods, contexts and defects. As code synthesis and debugging techniques based on machine learning have become popular, it is informative to consider how views on perennial issues in program repair have changed, or remained static, over time. This retrospective discusses the issues of repair quality (including the role of tests), use cases for automated repairs (including the role of humans), and why these approaches work at all.

关键词： Maintenance engineering Computer bugs Software Codes Standards Manuals Debugging Testing Genetic programming Evolution (biology) Automatic programming corrections testing and debugging evolutionary computation

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Machine-Learning-Assisted Optimization for Antenna Geometry Design

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2024年第3期72卷 2083-2095页

作者： Wu, Qi Chen, Weiqi Yu, Chen Wang, Haiming Hong, Wei Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Purple Mt Labs Pervas Commun Res Ctr Nanjing 211111 Peoples R China

A machine-learning-assisted optimization (MLAO) method for antenna geometry design (AGD) (MLAO-AGD) is proposed. By combining machine learning (ML) methods, including a convolutional neural network (CNN) and Gaussian process regression (GPR), MLAO-AGD achieves great efficient improvement compared with conventional evolutionary-algorithm-assisted AGD methods. The ML methods are introduced to build surrogate models between the antenna geometry and the antenna performance and then provide predictions of potential designs during optimization. The ML-based surrogate model is iteratively updated by verified optimization results using full-wave simulations. Three antenna design examples, including multiband and broadband antenna element design tasks and a mutual coupling reduction design task, are presented to show the advantages of the proposed MLAO-AGD, which include the convergence speed and antenna performance.

关键词： Antennas Geometry Optimization evolutionary computation Broadband antennas Topology Microstrip antennas convolutional neural network (CNN) geometry machine learning (ML) mutual coupling

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evolutionary Multitasking for Costly Task Offloading in Mobile-Edge Computing Networks

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IEEE TRANSACTIONS ON evolutionary computation 2024年第2期28卷 338-352页

作者： Yang, Chen Chen, Qunjian Zhu, Zexuan Huang, Zhi-An Lan, Shulin Zhu, Liehuang Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China City Univ Hong Kong Dongguan Res Off Dongguan 523000 Peoples R China City Univ Hong Kong Shenzhen Res Inst Multimedia Software Engn Res Ctr Shenzhen 518060 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100081 Peoples R China

The offloading of computation-intensive tasks to an edge server near resource-constrained mobile devices can provide improved application performance and user experience. However, with the rapid growth of mobile devices connected to the edge server, it is challenging to directly obtain an optimal task offloading scheme due to increasing computational cost and problem scale. In this study, we model the costly task offloading problem (CTOP) in mobile-edge computing networks to achieve efficient joint optimization of energy consumption and processing latency for mobile devices. Inspired by the success of evolutionary multitasking in solving complex optimization problems by leveraging the experience of simple optimization problems, we develop a novel multitasking framework whose effectiveness is demonstrated in solving the CTOP. In this framework, auxiliary tasks are created to optimize the local processing overhead and the edge processing overhead of task offloading. On this basis, we propose an effective multitask evolutionary algorithm that includes segmented knowledge transfer and auxiliary task update. Specifically, source and extended decision variables are considered as different knowledge to be utilized, while the auxiliary tasks are allowed to be updated dynamically. Related knowledge that is learned from cheap and simple auxiliary tasks promotes the evolutionary search for CTOP. Experimental results verify the effectiveness of knowledge transfer. Compared to existing multitasking and single-tasking algorithms, the proposed algorithm shows competitive performance in CTOP instances and achieves better comprehensive performance in terms of energy consumption and processing latency.

关键词： Task analysis Optimization Multitasking evolutionary computation Resource management Knowledge transfer Search problems Auxiliary task evolutionary multitasking (EMT) mobile-edge computing (MEC) task offloading

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evolutionary Architecture Search for Generative Adversarial Networks Based on Weight Sharing

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IEEE TRANSACTIONS ON evolutionary computation 2024年第3期28卷 653-667页

作者： Xue, Yu Tong, Weinan Neri, Ferrante Chen, Peng Luo, Tao Zhen, Liangli Wang, Xiao Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Nanjing 210044 Peoples R China Univ Surrey Dept Comp Sci NICE Res Grp Guildford GU2 7XH England Natl Inst Adv Ind Sci & Technol Tokyo 1350064 Japan RIKEN Ctr Computat Sci Kobe 6500047 Japan Agcy Sci Technol & Res Singapore Singapore Oak Ridge Natl Lab Oak Ridge TN 37831 USA

Generative adversarial networks (GANs) are a powerful generative technique but frequently face challenges with training stability. Network architecture plays a significant role in determining the final output of GANs, but designing a fine architecture demands extensive domain expertise. This article aims to address this issue by searching for high-performance generator's architectures through neural architecture search (NAS). The proposed approach, called evolutionary weight sharing GANs (EWSGAN), is based on weight sharing and comprises two steps. First, a supernet of the generator is trained using weight sharing. Second, a multiobjective evolutionary algorithm (MOEA) is employed to identify optimal subnets from the supernet. These subnets inherit weights directly from the supernet for fitness assessment. Two strategies are used to stabilize the training of the generator supernet: 1) a fair single-path sampling strategy and 2) a discarding strategy. Experimental results indicate that the architecture searched by our method achieved a new state-of-the-art among NAS-GAN methods with a Fr & eacute;chet inception distance (FID) of 9.09 and an inception score (IS) of 8.99 on the CIFAR-10 dataset. It also demonstrates competitive performance on the STL-10 dataset, achieving FID of 21.89 and IS of 10.51.

关键词： Training Generative adversarial networks Computer architecture Generators Network architecture Search problems Optimization evolutionary computation generative adversarial networks (GANs) generative model neural architecture search (NAS)

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