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evolutionary algorithm for the Traveling Salesman Problem With Innovative Encoding on Hybrid Quantum-Classical Machines

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IEEE ACCESS 2025年 13卷 54223-54239页

作者： Saini, Ravi Mani, Ashish Prasad, M. S. Bhattacharyya, Siddhartha Platos, Jan Amity Univ Dept Comp Sci & Engn ASET Noida 201301 Uttar Pradesh India Amity Univ Amity Innovat & Design Ctr Noida 201301 Uttar Pradesh India Amity Univ Amity Inst Space Sci & Technol Noida 201301 Uttar Pradesh India VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic

The Traveling Salesman Problem (TSP) is a widely studied NP-complete optimization challenge with significant theoretical and practical implications. This study proposes a hybrid quantum-classical framework using a Quantum-Inspired evolutionary algorithm (QEA) with Sort Gray Binary Encoding to solve the TSP. The proposed method guarantees the generation of valid TSP tours by eliminating invalid solutions. It employs quantum superposition with intrinsic randomness to enhance computational efficiency and scalability. The framework was implemented on cloud-based NISQ platforms, including IBM Quantum and AWS Braket, demonstrating its practicality and effectiveness. Experimental evaluations revealed that the proposed framework successfully solved TSP instances with up to 15 cities, achieving superior performance compared to classical methods and showcasing its ability to scale under NISQ constraints. The results also highlight the potential of hybrid quantum-classical approaches to overcome hardware limitations in current quantum systems. This study establishes a robust hybrid methodology for solving combinatorial optimization problems. It also sets a benchmark for leveraging the capabilities of NISQ-era quantum devices in real-world applications, thereby providing a foundation for future research in hybrid quantum-classical optimization techniques.

关键词： Urban areas Quantum computing Logic gates Encoding evolutionary computation Traveling salesman problems Qubit Mathematical models Quantum system Hardware evolutionary algorithm gray encoding hybrid algorithms noisy intermediate-scale quantum quantum computing traveling salesman problem

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evolutionary algorithm framework for optimizing truck scheduling in multi-dock truck cross-docking centers

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evolutionary INTELLIGENCE 2025年第1期18卷 1-21页

作者： Nogueira, Thiago Henrique Coutinho, Felipe Provezano Peixoto, Maria Gabriela Mendonca Carrano, Eduardo Gontijo Ravetti, Martin Gomez UFV Inst Ciencias Exatas Rio Paranaiba Brazil Univ Sao Paulo Inst Ciencias Matemat & Computacao Sao Paulo Brazil Univ Fed Minas Gerais Dept Engn Elect Belo Horizonte Brazil Univ Fed Minas Gerais Dept Ciencia Computacao Belo Horizonte Brazil

Cross-docking optimizes logistics by reducing storage and handling times in warehouses, where cargos are unloaded from inbound trucks and loaded directly onto outbound vehicles. This paper addresses the critical challenge of minimizing makespan in a multi-dock, parallel machine setting within cross-docking centers. We propose a novel framework that integrates an evolutionary algorithm (EA) with a machine learning (ML) based hyperparameter tuning mechanism to optimize truck sequencing. This study fills the research gap by offering a quantifiable improvement over traditional heuristic methods, delivering up to a 37% improvement in the GAP metric compared to state-of-the-art techniques. It also achieves a 30% enhancement over the PCH constructive heuristic used for generating initial solutions. Additionally, our ML-based tuning strategy provides up to a 21% performance increase over static tuning methods. Notably, these improvements are attained while maintaining a competitive computational time as reported in the literature.

关键词： Truck Scheduling Cross-docking evolutionary algorithm Logistics

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A Tractive Population-Assisted Dual-Population and Two-Phase evolutionary algorithm for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2025年第1期29卷 31-45页

作者： Xie, Shumin Li, Kangshun Wang, Wenxiang Wang, Hui Peng, Chaoda Jalil, Hassan South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518172 Peoples R China

Both dual-population and two-phase strategies are effective for utilizing infeasible solution information and significantly enhancing the ability of algorithms to solve constrained multiobjective optimization problems. However, most existing algorithms tend to underperform when facing problems with complex constraints. To address these issues, a constrained multiobjective evolutionary algorithm named DPTPEA, which combines dual-population and two-phase strategies, is proposed in this article. DPTPEA employs two collaborative populations [the exploitive population (expPop) and the tractive population (tracPop)] and divides the evolutionary process of the tracPop into two phases (Phase 1 and Phase 2). In Phase 1, the tracPop ignores constraints and drags the expPop across the infeasible region by sharing offspring information. In Phase 2, the tracPop adopts the epsilon-constrained method to converge toward the constrained Pareto front and to guide the expPop exploiting different feasible regions. Moreover, a dynamic cooperation strategy, a boundary point direction sampling strategy, and a dynamic environmental selection are proposed to improve the exploration ability of tracPop for solving complex problems. Comprehensive experiments on three popular test suites demonstrate that DPTPEA outperforms seven state-of-the-art algorithms on most test problems.

关键词： Statistics Sociology Optimization evolutionary computation Convergence Classification algorithms Pareto optimization Constrained multiobjective optimization dual-population evolutionary algorithm two-phase

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Multi-objective hub location for urban air mobility via self-adaptive evolutionary algorithm

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ADVANCED ENGINEERING INFORMATICS 2025年 64卷

作者： Zhang, Chunxiao Du, Wenbo Guo, Tong Yu, Rongjie Song, Tao Li, Yumeng Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China State Key Lab CNS ATM Beijing 100191 Peoples R China Tongji Univ Coll Transportat Engn Shanghai 201804 Peoples R China Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China

As urbanization increases and city populations grow, traditional metropolitan logistics face challenges in managing the rising volume of goods efficiently. Urban air mobility (UAM) offers a promising solution for urban goods delivery by enabling fast and direct aerial transport between key hubs, forming the basis for urban air logistics (UAL). However, most existing studies on UAL hub location primarily focus on solely optimizing economic factors such as cost and efficiency, often overlooking critical safety concerns associated with third-party risks due to UAM operations. To fill this gap, we develop a novel multi-objective mixed- integer programming model that integrates a comprehensive urban air delivery risk assessment with the hub location problem. Our model simultaneously considers economic costs and third-party safety risks as bi-objectives, providing trade-off solutions between economic factors and safety concerns for decision makers. Furthermore, to effectively solve the model, we propose an improved non-dominated sorting genetic algorithm with adaptive operator selection (INSGA-AOS). This algorithm incorporates a bi-level encoding scheme, nine problem-specific search operators for crossover and mutation, and an adaptive operator selection mechanism based on multi-armed bandits (MABs) to dynamically identify the optimal combinations of crossover and mutation operators. Experimental results on instances generated from a benchmark dataset demonstrate that our proposed algorithm significantly outperforms existing methods in generating high-quality Pareto front. Further analysis verifies the effectiveness of different components within the proposed method.

关键词： Urban air mobility logistics Third-party risk Multi-objective hub location evolutionary algorithm Adaptive operator selection

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A hybrid evolutionary algorithm to improve task scheduling and load balancing in fog computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第1期28卷 1-26页

作者： Yu, Dongxian Zheng, Weiyong Henan Polytech Coll Modern Informat Technol Zhengzhou 450046 Henan Peoples R China Shanghai Univ Int Sch Foreign Languages Business & Econ Shanghai 201620 Peoples R China

This paper introduces a hybrid evolutionary task scheduling and VM placement algorithm (HETSVP) designed for dependable fog computing task scheduling and VM placement. We address the optimization of task execution time and resource balance concurrently by integrating an improved particle swarm optimization algorithm with a new VM placement strategy. We utilize a direct binary encoding technique, where the particle's location information is expressed using 0 and 1, and particle velocity ranges within [0, 1]. In the context of discrete particle swarm, each particle's position will signify a potential task scheduling plan. Also we provide the adaptive contraction factor, which improves the efficiency of the particle swarm optimization approach. On the other hand, due to the importance of VM placing we introduce a new placement strategy that according to the available of PMs resources, the VM placement operation is performed. We then conduct simulation experiments in ifogsim environment to assess the performance of HETSVP. The results demonstrate that compared to the MinMin and MaxMin algorithm, HETSVP reduces makespan by 11% and enhances energy consumption by 15%. Additionally, compared to Genetic and ACO algorithms, HETSVP achieves 13%, 5% reduction in makespan, and improves energy consumption by 12%, 5%. Moreover, the simulations indicate that the proposed method can improve the fog environment's reliability. Furthermore, compared to other methods, HETSVP exhibits a better degree of imbalance and makespan results.

关键词： Fog computing IoT evolutionary algorithm Scheduling VM placement

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An evolutionary algorithm driving by dimensionality reduction operator and knowledge model for the electric vehicle routing problem with flexible charging strategy

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Yang, Bihao Ren, Teng Yu, Huijuan Chen, Jie Wang, Yaya Cent South Univ Forestry & Technol Sch Logist Changsha 410004 Peoples R China

The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development *** today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.

关键词： evolutionary algorithm Reinforcement learning Knowledge model Electric vehicle routing problem scheduling Flexible charging strategy

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A multi-population evolutionary algorithm based on knowledge transfer for constrained many-objective optimization

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ENGINEERING OPTIMIZATION 2025年第3期57卷 813-843页

作者： Ge, Wenlong Zhang, Shanxin Song, Weida Wang, Wei Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China

Constrained Many-objective Optimization Problems (CMaOPs) are challenging in handling objectives and constraints simultaneously. Here, a novel Constrained Many-objective Optimization evolutionary algorithm (CMaOEA) based on Multi-population, Knowledge transfer and Improved environmental selection called CMaMKI is proposed to handle CMaOPs. The proposed framework evolves a task population to solve the original CMaOP and evolves another population to solve a helper problem derived from the original one. To assist solving the original CMaOP, a knowledge expression and transfer strategy is designed to share useful information in the helper population with the task population. Meanwhile, to balance population convergence, diversity and feasibility, an enhanced environmental selection strategy is devised by combining the epsilon-constrained technique, theta-dominance and subregional density evaluation. The proposed algorithm is evaluated and contrasted with six state-of-the-art algorithms on a set of benchmark CMaOPs. The experimental results demonstrate the superiority and competitiveness of the proposed method.

关键词： Constrained many-objective optimization evolutionary algorithm environmental selection multi-population knowledge transfer

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Machine Learning-Assisted Multiobjective evolutionary algorithm for Routing and Packing

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2025年第2期29卷 317-330页

作者： Liu, Fei Zhang, Qingfu Zhu, Qingling Tong, Xialiang Yuan, Mingxuan City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Huawei Noahs Ark Lab Shenzhen 518129 Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

Many combinatorial multiobjective optimization problems involve very costly-to-evaluate objectives and constraints. It is very difficult, if not impossible, for traditional heuristics to solve these problems with an acceptable amount of computational time. In this article, we show that offline machine learning can be very useful to assist multiobjective evolutionary algorithms to tackle this kind of problem. We take a complicated real-life multiobjective routing-packing problem as the test bed. We propose to use offline machine learning methods to replace time-consuming packing heuristics for packing feasibility prediction. Experiments show that the machine learning models can be 1000 times faster than some commonly used packing heuristics and their accuracy can be as high as 98%. We adopt decomposition-based multiobjective evolutionary algorithmic to decompose the problem into a number of single objective subproblems and solve them in a collaborative manner. We propose an encoding strategy to represent each routing scheme and use genetic operators to generate new routes. Experimental studies have been conducted on 100 instances from HUAWEI's real-world logistics application and two test suites from the literature. Our proposed method can solve each HUAWEI instance in around 1 min. Our solutions on the two test suites are comparable to other existing algorithms, and the overall computational cost of our method is significantly lower than others.

关键词： Routing Machine learning evolutionary computation Optimization Machine learning algorithms Encoding Vehicle routing Bin packing evolutionary algorithm machine learning multiobjective optimization vehicle routing

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A Two-Individual evolutionary algorithm for Cumulative Capacitated Vehicle Routing With Single and Multiple Depots

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2025年第2期29卷 505-518页

作者： Zou, Yuji Hao, Jin-Kao Wu, Qinghua Univ Angers Dept Comp Sci LERIA F-49045 Angers France Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

The cumulative capacitated vehicle routing problem (CCVRP) with single or multiple depots (MDCCVRP) is a variant of the popular capacitated vehicle routing problem. Instead of minimizing the total travel time, the objective here is to minimize the sum of all customers' waiting times. This problem has a variety of real-world applications, especially in post-disaster humanitarian relief. To solve the challenging CCVRP and MDCCVRP, we propose a unified two-individual evolutionary algorithm that follows the memetic search framework. The algorithm integrates several key features: a two-individual population mechanism to accelerate convergence while maintaining population diversity, a dedicated edge assembly crossover to generate high-quality offspring, and an adaptive feasible and infeasible local search to achieve a balanced exploration between feasible and infeasible solutions. The algorithm is evaluated on 39 CCVRP instances and 78 MDCCVRP instances commonly used in the literature. Computational results show that for the CCVRP, the algorithm outperforms the leading algorithms by achieving improved best results (new upper bound) for 13 instances and matching the best results for 23 other instances. For the MDCCVRP, the algorithm achieves nine improved best results and matches the best results for the remaining instances. The critical components of the algorithm are investigated to understand their contributions.

关键词： evolutionary computation Vehicle routing Statistics Sociology Routing Search problems Memetics Combinatorial optimization cumulative capacitated routing evolutionary algorithm heuristics vehicle routing

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Compound Hydrological Forecasting Model by Long Short-term Memory Network Coupled with Adaptive Mode Decomposition and evolutionary algorithm

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WATER RESOURCES MANAGEMENT 2025年第6期39卷 2651-2672页

作者： Feng, Zhong-kai Liu, Wen-jie Niu, Wen-jing Yang, Tao Wang, Wen-chuan Wang, Sen Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Yangtze Inst Conservat & Dev Nanjing 210098 Jiangsu Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Changjiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China North China Univ Water Resources & Elect Power Coll Water Resources Henan Key Lab Water Resources Conservat & Intens U Zhengzhou 450046 Peoples R China

Owing to increasing human activities and climate events over the past few decades, reliable hydrological forecasting has become a critical yet challenging task in water resources planning, reservoir operation, and water supply management. This study presents a hybrid artificial intelligence-based approach for hydrological prediction, combining signal decomposition techniques and optimization algorithms. Specifically, Variational Mode Decomposition (VMD) is employed to capture the irregular and dynamic features of the original hydrological time series. Subsequently, a Long Short-Term Memory (LSTM) network is applied to develop an effective forecasting model for each decomposed component, with the Whale Optimization algorithm (WOA) employed to identify the optimal parameter set for each LSTM model. The proposed method is tested on runoff datasets from three hydrological stations in China, and its feasibility is demonstrated through several quantitative performance metrics. Extensive simulations reveal that the proposed model outperforms traditional models across various scenarios. With high accuracy and stability, this data-driven approach provides a robust solution to the complex problem of hydrological forecasting. Furthermore, the integration of signal decomposition and evolutionary algorithms enhances the performance of artificial intelligence models in regression tasks.

关键词： Hydrological time series prediction Signal decomposition Long short-term memory network Artificial intelligence evolutionary algorithm

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