Hybrid Discrete Differential Evolution Algorithm for Motor Train-Sets Scheduling

Author(s):  
Chunmei Zhang ◽  
Haibin Yang ◽  
Jiajun Li
2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Zhou-zhou Liu ◽  
Shi-ning Li

To reconstruct compressed sensing (CS) signal fast and accurately, this paper proposes an improved discrete differential evolution (IDDE) algorithm based on fuzzy clustering for CS reconstruction. Aiming to overcome the shortcomings of traditional CS reconstruction algorithm, such as heavy dependence on sparsity and low precision of reconstruction, a discrete differential evolution (DDE) algorithm based on improved kernel fuzzy clustering is designed. In this algorithm, fuzzy clustering algorithm is used to analyze the evolutionary population, which improves the pertinence and scientificity of population learning evolution while realizing effective clustering. The differential evolutionary particle coding method and evolutionary mechanism are redefined. And the improved fuzzy clustering discrete differential evolution algorithm is applied to CS reconstruction algorithm, in which signal with unknown sparsity is considered as particle coding. Then the wireless sensor networks (WSNs) sparse signal is accurately reconstructed through the iterative evolution of population. Finally, simulations are carried out in the WSNs data acquisition environment. Results show that compared with traditional reconstruction algorithms such as StOMP, the reconstruction accuracy of the algorithm proposed in this paper is improved by 36.4-51.9%, and the reconstruction time is reduced by 15.1-31.3%.


2013 ◽  
Vol 303-306 ◽  
pp. 2227-2230 ◽  
Author(s):  
Ling Juan Hou ◽  
Zhi Jiang Hou

Aiming at the stochastic vehicle routing problems with simultaneous pickups and deliveries, a novel discrete differential evolution algorithm is proposed for routes optimization. The algorithm can directly be used for the discrete domain by special design. Computational simulations and comparisons based on a medium-sized problem of SVRPSPD is provided. Results demonstrate that the proposed algorithm obtains better results than the basic differential evolution algorithm and the existing genetic algorithm.


Author(s):  
Hossam M. J. Mustafa ◽  
Masri Ayob ◽  
Mohd Zakree Ahmad Nazri ◽  
Sawsan Abu-Taleb

The Container Pre-marshalling Problem (CPMP) has the significant effect of reducing ship berthing time, and can help in increasing terminal turnover rate. In order to solve the CPMP, this research proposes a Multi-objectives Memetic Discrete Differential Evolution algorithm (MODDE). To date, existing research in CPMP only focuses on single-objective approaches. However, this is not a suitable approach due to the considerable effort required to validate the hard constraints of CPMP. Therefore, this work aims at addressing the effect of minimizing the number of miss-overlaid containers on the total number of movements in building the final feasible bay layout by embedding it in the multi-objectives evaluation function. The proposed algorithm combines the Discrete Differential Evolution mutation with the Memetic Algorithm evolutionary steps in order to find high quality CPMP solutions, achieve high convergence rate and avoid premature convergence and local optima problems. In addition, it improves the exploration and exploitation capabilities of the algorithm. The standard pre-marshalling benchmark dataset (i.e., Bortfeldt-Forster) is used to evaluate the effectiveness of the proposed algorithm. The experimental results reveal that the proposed MODDE algorithm can find good solutions on instances of the standard pre-marshalling benchmarks. This demonstrates that using the multi-objectives approach with a combination of the Discrete Differential Evolution mutation and the Memetic Algorithm evolutionary is a suitable approach for solving multi-objectives CPMP.  


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