Solving hybrid charging strategy electric vehicle based dynamic routing problem via evolutionary multi-objective optimization

Wang, Zhenzhong; Yang, Kai; Jiang, Min; Yao, Junfeng; Xiong, Neal N.; Yen, Gary G.

doi:10.1016/j.swevo.2021.100975

Cited by 26 publications

(4 citation statements)

References 40 publications

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“…Currently, swarm intelligence and optimization algorithms have been widely concerned in various applications due to their efficient optimization performance [10][11][12]. They are successfully used in intelligent transportation, machine learning, process control, economic forecasting, and engineering optimization [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

Zong

Wang

et al. 2022

Electronics

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A two-layer artificial fish swarm evacuation model based on heterogeneous pheromones is presented in this paper. Firstly, the movements of evacuees are simulated by the behaviors of an artificial fish swarm, including preying, swarming, and following. Then, the positive feedback mechanism of heterogeneous pheromones is introduced to improve evacuation performance. Based on the interaction and communication mechanisms of biological groups of social networks in nature, the perceptual and cooperative model among individuals and between individuals and the environment is established. An optimization scheme based on fish swarms and heterogeneous pheromones is proposed. The simulation and experimental results show that the two-layer evacuation model can optimize the spatial-temporal distribution of people and can finally achieve better evacuation plans. The proposed model and algorithm can provide effective guidance for emergency safety responses and robot cooperative control in intelligent robot systems.

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Section: Related Workmentioning

confidence: 99%

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

Zong

Wang

et al. 2022

Electronics

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show abstract

“…However, the drawback is that, during the optimization process, multi-objective optimization algorithms are prone to settling on local optima. In this regard, the use of the crossover strategy and local search method improves the problem to a certain extent and enhances the global optimization ability of multi-objective optimization algorithms [28], [29], [30], but most existing crossover strategies are only a simple reorganization of the encoding of solutions and do not refer to the ''coding structure'' of the superior solution, making the crossover operation more random. Moreover, the equal probability selection of local search strategy ignores the search knowledge generated during the search process [31], [32], [33], which leads to the problem that the algorithm generates ''blind'' search and is prone to fall into the problem of local optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning-Based Adaptive Search for Solving Time-Dependent Green Vehicle Routing Problem

Yue,

Ma,

Shi

et al. 2024

IEEE Access

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The time-dependent green vehicle routing problem with time windows is a further deepening of the research on vehicle routing problems with time windows. Its simultaneous consideration of vehicle transportation time, carbon emissions, and customer satisfaction under time-dependent variables makes it more challenging to solve than traditional vehicle routing problems. This work proposes a multi-objective optimization algorithm that combines the learnable crossover strategy and the adaptive search strategy based on reinforcement learning to overcome the local optima, poor convergence, and reduced variety of solutions that plague the multi-objective optimization algorithms when solving this problem. The proposed approach solves the problem in two stages: In the first stage, a hybrid initialization strategy is used to generate initial solutions with high quality and diversity, and crossover strategies are used to further explore the solution space and improve convergence by learning the characteristics of pareto solutions. In the second stage, the adaptive search is designed and used for learning and searching in the later stage of the algorithm. The experimental results show better solution quality obtained by the proposed approach, and the effectiveness and superiority of the proposed approach over existing methods in terms of solution convergence and diversity are demonstrated through experimental comparisons.

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“…Inspired by the distinctive competency of evolutionary algorithms for large-scale combinatorial optimization problems (Zhou et al 2021;Zhao et al 2020;Wang et al 2022b), we develop a dedicated multi-objective evolutionary algorithm-based framework for generating diagnostic and actionable explanations. The generated explanations can satisfy the following three objectives simultaneously: compactness, bias attribution, and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks

Wang,

Zeng,

Lin

et al. 2024

AAAI

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A plethora of fair graph neural networks (GNNs) have been proposed to promote algorithmic fairness for high-stake real-life contexts. Meanwhile, explainability is generally proposed to help machine learning practitioners debug models by providing human-understandable explanations. However, seldom work on explainability is made to generate explanations for fairness diagnosis in GNNs. From the explainability perspective, this paper explores the problem of what subgraph patterns cause the biased behavior of GNNs, and what actions could practitioners take to rectify the bias? By answering the two questions, this paper aims to produce compact, diagnostic, and actionable explanations that are responsible for discriminatory behavior. Specifically, we formulate the problem of generating diagnostic and actionable explanations as a multi-objective combinatorial optimization problem. To solve the problem, a dedicated multi-objective evolutionary algorithm is presented to ensure GNNs' explainability and fairness in one go. In particular, an influenced nodes-based gradient approximation is developed to boost the computation efficiency of the evolutionary algorithm. We provide a theoretical analysis to illustrate the effectiveness of the proposed framework. Extensive experiments have been conducted to demonstrate the superiority of the proposed method in terms of classification performance, fairness, and interpretability.

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Solving hybrid charging strategy electric vehicle based dynamic routing problem via evolutionary multi-objective optimization

Cited by 26 publications

References 40 publications

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

An Artificial Fish Swarm Scheme Based on Heterogeneous Pheromone for Emergency Evacuation in Social Networks

A Deep Reinforcement Learning-Based Adaptive Search for Solving Time-Dependent Green Vehicle Routing Problem

Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks

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