An improved crow search algorithm based on oppositional forgetting learning

Xu, Wei; Zhang, Ruifeng; Chen, Lei

doi:10.1007/s10489-021-02701-y

Cited by 9 publications

(6 citation statements)

References 54 publications

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“…However, few scholars have applied CSA in the field of electric vehicle routing problems. Although CSA offers many benefits, such as simple parameter tuning, its slow convergence speed and low convergence accuracy are still considered shortcomings, and scholars have suggested improving CSA to address these issues (Xu et al ., 2022). Therefore, hybrid heuristic algorithms that combine different algorithms are proposed to improve performance, which can be parallel or inseparable in parts (Ramachandran et al ., 2022; Tang et al ., 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

An electric vehicle routing model with charging stations consideration for sustainable logistics

Li,

Lim,

Xiong

et al. 2023

IMDS

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PurposeRecently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.Design/methodology/approachThis study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.FindingsThe result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.Research limitations/implicationsThe optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.Originality/valueIn prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

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Section: Literature Reviewmentioning

confidence: 99%

An electric vehicle routing model with charging stations consideration for sustainable logistics

Li,

Lim,

Xiong

et al. 2023

IMDS

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show abstract

“…These functions have been subject to be shifted and rotated, making it difficult to find their global optimum values. Solving these functions' global optimum solutions in low dimensions makes it possible to evaluate the performance of metaheuristic algorithms effectively(W. Xu, Zhang, & Chen, 2022). From the data in the table, it can be observed that except for function F 19 where the HI-WHO ranks second in terms of the standard deviation (Std), surpassed only by the IWHOLF algorithm, the HI-WHO achieves the best results in the remaining test cases.…”

Section: Analysis Of Optimization Accuracymentioning

confidence: 99%

A Novel Algorithm for Robot Path Planning Based on Hybrid Strategy Improved Wild Horse Optimizer

Zhao,

Luo,

2023

Preprint

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In order to solve the path planning problem of mobile robots, a novel hybrid strategy improved wild horse optimizer (HI-WHO) is proposed in this paper. The algorithm utilizes Sobol sequence to initialize the population, which ensures a uniform distribution of initial solutions in the search space. It also integrates Lévy flight strategy and nonlinear dynamic adaptive factor to balance the exploration and exploitation ability and improve the search efficiency and quality at different stages of the algorithm. In addition, the algorithm can ensure the global search capability and the ability to jump out of the local optimum by using the lens imaging opposition-learning strategy and greedy mechanism. In the simulation experiments, 20 benchmark functions are selected to verify the effectiveness of the proposed method. Finally, the improved algorithm, combined with cubic B-Spline interpolation, solves the path planning problem by establishing the mathematical model for mobile robot path planning, and the performance is evaluated by conducting simulation experiments in a simple experimental environment and a real environment with grid map. The results show that the HI-WHO has good stability and optimal comprehensive performance, showcasing its effectiveness in addressing the robot path planning problem.

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“…CEC2019 test functions and features are given in Table 9. The mean and standard deviation values obtained as a result of this test process were compared with the algorithms in the literature (Xu et al 2022). These algorithms are CSA (Hussien et al 2020), BOA (Arora and Singh 2019), MFO (Mirjalili 2015), BA (Yang and He 2013), WOA (Mirjalili and Lewis 2016), SCA (Mirjalili 2016), PSOGSA (Mirjalili and Hashim 2010), AGWO (Qais et al 2018), OBSCA (Abd Elaziz et al 2017, and EGWO (Joshi and Arora 2017) algorithm.…”

Section: Gauss Mapmentioning

confidence: 99%

“…In Table 10, CSO refers to the best-performing 𝐶𝑆𝑂3 𝑃𝑖𝑒𝑐𝑒𝑤𝑖𝑠𝑒 + CSO4 Sinusoidal in previous comparisons. Results for other algorithms are taken directly from the study of Xu et al (Xu et al 2022). For a fair comparison, the population size is 50 and the maximum iteration is 10,000 in all algorithms.…”

Section: Gauss Mapmentioning

confidence: 99%

Kaoti̇k Yilan Opti̇mi̇ze Edi̇ci̇

YILDIZDAN

2023

Afyon Kocatepe University Journal of Sciences and Engineering

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Metaheuristic algorithms provide approximate or optimal solutions for optimization problems in a reasonable time. With this feature, metaheuristic algorithms have become an impressive research area for solving difficult optimization problems. Snake Optimizer is a population-based metaheuristic algorithm inspired by the mating behavior of snakes. In this study, different chaotic maps were integrated into the parameters of the algorithm instead of random number sequences to improve the performance of Snake Optimizer, and Snake Optimizer variants using four different chaotic mappings were proposed. The performances of these proposed variants for eight different chaotic maps were examined on classical and CEC2019 test functions. The results revealed that the proposed algorithms contribute to the improvement of Snake Optimizer performance. In the comparison with the literature, the proposed Chaotic Snake Optimizer algorithm found the best mean values in many functions and took second place among the algorithms. As a result of the tests, Chaotic Snake Optimizer has been shown to be a promising, successful, and preferable algorithm.

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An improved crow search algorithm based on oppositional forgetting learning

Cited by 9 publications

References 54 publications

An electric vehicle routing model with charging stations consideration for sustainable logistics

An electric vehicle routing model with charging stations consideration for sustainable logistics

A Novel Algorithm for Robot Path Planning Based on Hybrid Strategy Improved Wild Horse Optimizer

Kaoti̇k Yilan Opti̇mi̇ze Edi̇ci̇

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