Memory-Based Ant Colony System Approach for Multi-Source Data Associated Dynamic Electric Vehicle Dispatch Optimization

Shi, Lin; Zhan, Zhi‐Hui; Dian-nong, Liang; Zhang, Mengjie

doi:10.1109/tits.2022.3150471

Cited by 34 publications

(10 citation statements)

References 55 publications

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“…Keeping this in mind, we will target our future work on the new inter-task similarity measurement mechanism. In addition, we will apply the BoKT framework to other evolutionary computation algorithms like particle swarm optimization [52]- [54] and ant colony optimization [55]- [57], and also extend the BoKTbased algorithms to help efficiently solve some challenging real-world optimization problems [58]- [60].…”

Section: Discussionmentioning

confidence: 99%

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Jiang

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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Many-task optimization problem is a kind of challenging multi-task optimization problem with more than three tasks. Two significant issues in solving many-task optimization problems are measuring inter-task similarity and transferring knowledge among similar tasks. However, most existing algorithms only use a single similarity measurement, which cannot accurately measure the inter-task similarity because the inter-task similarity is a concept with multiple different aspects. To address this limitation, this paper proposes a bi-objective knowledge transfer framework, which aims firstly to accurately measure different types of inter-task similarity using two different measurements and secondly to effectively transfer knowledge with different types of similarity via specific strategies. To achieve the first goal, a bi-objective measurement is designed to measure inter-task similarity from two different aspects, including shape similarity and domain similarity. To achieve the second goal, a similarity-based adaptive knowledge transfer strategy is designed to choose the suitable knowledge transfer strategy based on the type of inter-task similarity. We compare the bi-objective knowledge transfer framework-based algorithms with several state-of-the-art algorithms on two challenging many-task optimization test suites with 16 instances and on real-world many-task optimization problems with up to 500 tasks. The experimental results show that the proposed algorithms generally outperform the compared algorithms.

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Section: Discussionmentioning

confidence: 99%

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Jiang

Zhan

Tan

et al. 2023

IEEE Trans. Evol. Computat.

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“…This study proposes an optimization method for EV charging scheduling based on the ADS-ACO algorithm to address the issue of reduced battery life and continuous decline in endurance of EVs in current traffic congestion scenarios. The traditional ACO algorithm primarily employs the positive feedback mechanism of the residual pheromones along the path between ants for information transmission, thereby exhibiting robust resilience and the capacity to identify superior solutions [16][17][18]. The process is shown in Figure 1.…”

Section: Analysis Of Ant Colony Algorithm Based On Adaptive Dynamic S...mentioning

confidence: 99%

Application of Improved Ant Colony Algorithm in Optimizing the Charging Path of Electric Vehicles

2024

WEVJ

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In current traffic congestion scenarios, electric vehicles (EVs) have the problem of reduced battery life and continuous decline in endurance. Therefore, this study proposes an optimization method for electric vehicle charging scheduling based onthe ant colony optimization algorithm with adaptive dynamic search (ADS-ACO), and conducts experimental verification on it. The experiment revealed that in the four benchmark functions, the research algorithm has the fastest convergence speed and can achieve convergence in most of them. In the validation of effectiveness, the optimal solution for vehicle time consumption under the ADS-ACO algorithm in the output of the algorithm with a stationary period and a remaining battery energy of 15 kW·h was 2.146 h in the regular road network. In the initial results of 15 kW·h under changes in road conditions from peak to peak periods, the total energy consumption of vehicles under the research algorithm was 4.678 kW·h and 4.656 kW·h under regular and irregular road networks, respectively. The change results were 4.509 kW·h and 4.656 kW·h, respectively. The initial results of 10 kW·h were 4.755 kW·h and 4.873 kW·h, respectively. The change results were 4.461 kW·h and 4.656 kW·h, respectively, which are lower than the comparison algorithm. In stability verification, research algorithms can find the optimal path under any conditions. The algorithm proposed in the study has been demonstrated to be highly effective and stable in electric vehicle charging path planning. It represents a novel solution for electric vehicle charging management and is expected to significantly enhance the range of electric vehicles in practical applications.

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“…Various ACO algorithms have been proposed for NP-hard optimization problems [33,34]. For instance, ACO has shown its efficiency and reliability in many optimization problems, including software project scheduling and staffing [35,36], vehicle routing problems [37,38], capacitated arc routing problems [39], virtual machine placement [40], supply chain management [41], new energy vehicle scheduling [42], and cloud workflow scheduling [43]. More importantly, ACO has shown its efficiency in solving static TSP [44,45].…”

Section: Introductionmentioning

confidence: 99%

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

Wang¹,

Zhu²,

Jiang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The dynamic traveling salesman problem (DTSP) is significant in logistics distribution in real-world applications in smart cities, but it is uncertain and difficult to solve. This paper proposes a scheme library-based ant colony optimization (ACO) with a two-optimization (2-opt) strategy to solve the DTSP efficiently. The work is novel and contributes to three aspects: problem model, optimization framework, and algorithm design. Firstly, in the problem model, traditional DTSP models often consider the change of travel distance between two nodes over time, while this paper focuses on a special DTSP model in that the node locations change dynamically over time. Secondly, in the optimization framework, the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment. The framework of offline optimization and online application is proposed due to the fact that the environmental change in DTSP is caused by the change of node location, and therefore the new environment is somehow similar to certain previous environments. This way, in the offline optimization, the solutions for possible environmental changes are optimized in advance, and are stored in a mode scheme library. In the online application, when an environmental change is detected, the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity. Thirdly, in the algorithm design, the ACO cooperates with the 2-opt strategy to enhance search efficiency. To evaluate the performance of ACO with 2-opt, we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms. The experimental results show that ACO with 2-opt can solve the DTSPs effectively.

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Memory-Based Ant Colony System Approach for Multi-Source Data Associated Dynamic Electric Vehicle Dispatch Optimization

Cited by 34 publications

References 55 publications

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization

Application of Improved Ant Colony Algorithm in Optimizing the Charging Path of Electric Vehicles

A Scheme Library-Based Ant Colony Optimization with 2-Opt Local Search for Dynamic Traveling Salesman Problem

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