scholarly journals A Novel Differential Evolution Algorithm for TWET-NFSSP with SDSTs and RDs

2012 ◽  
Vol 6-7 ◽  
pp. 748-756
Author(s):  
Bin Qian ◽  
Hua Bing Zhou ◽  
Rong Hu ◽  
Li Ping Wu
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Evaluation Method ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
De Algorithm ◽  
Weighted Earliness ◽  
Dependent Setup Times

A novel differential evolution (DE) algorithm, namely DE_TWET, is presented to deal with the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs). The criterion is to minimize a total weighted earliness/tardiness (TWET) cost function. The presented algorithm is a hybrid of DE, problem’s properties, and a special designed local search. In DE_TWET, DE is adopted to execute global search in the solution space, and the problem’s properties are utilized to give a speed-up evaluation method and construct the local search, and the special local search is designed to enhance the local search ability of DE. Experimental results and comparisons demonstrate the effectiveness and robustness of the presented algorithm.

scholarly journals An Enhanced Differential Evolution Algorithm with Fast Evaluating Strategies for TWT-NFSP with SSTs and RTs

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Rong Hu ◽  
Xing Wu ◽  
Bin Qian ◽  
Jian L. Mao ◽  
Huai P. Jin
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
Total Weighted Tardiness ◽  
Release Times ◽  
Evolution Algorithm ◽  
No Wait ◽  
Dependent Setup Times

The no-wait flow-shop scheduling problem with sequence-dependent setup times and release times (i.e., the NFSP with SSTs and RTs) is a typical NP-hard problem. This paper proposes an enhanced differential evolution algorithm with several fast evaluating strategies, namely, DE_FES, to minimize the total weighted tardiness objective (TWT) for the NFSP with SSTs and RTs. In the proposed DE_FES, the DE-based search is adopted to perform global search for obtaining the promising regions or solutions in solution space, and a fast local search combined with three presented strategies is designed to execute exploitation from these obtained regions. Test results and comparisons with two effective meta-heuristics show the effectiveness and robustness of DE_FES.

Optimization of Flow Shop Scheduling Problem Using Differential Evolution and Variable Neighborhood Search

2012 ◽  
Vol 590 ◽  
pp. 540-544
Author(s):  
Yan Ping Zhou
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Variable Neighbourhood Search ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Neighbourhood Search ◽  
Evolution Algorithm

This paper proposed a new differential evolution algorithm based on variable neighbourhood search which is called as VNSDE. In VNSDE, the operation of variable neighbourhood search is performed after three basic operations of differential evolution, which can enhance the global search and improve the convergence. VNSDE is applied for solving flow shop scheduling problem with the makespan criterion. Computational experiment is performed over a typical FSSP benchmark using VNSDE, GA and DE, and result shows that VNSDE has higher performance than GA and DE.

scholarly journals The Cellular Differential Evolution Based on Chaotic Local Search

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Qingfeng Ding ◽  
Guoxin Zheng
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Small Region ◽  
Population Diversity ◽  
Evolutionary Stage ◽  
Crossover Operator ◽  
De Algorithm ◽  
Solution Accuracy

To avoid immature convergence and tune the selection pressure in the differential evolution (DE) algorithm, a new differential evolution algorithm based on cellular automata and chaotic local search (CLS) or ccDE is proposed. To balance the exploration and exploitation tradeoff of differential evolution, the interaction among individuals is limited in cellular neighbors instead of controlling parameters in the canonical DE. To improve the optimizing performance of DE, the CLS helps by exploring a large region to avoid immature convergence in the early evolutionary stage and exploiting a small region to refine the final solutions in the later evolutionary stage. What is more, to improve the convergence characteristics and maintain the population diversity, the binomial crossover operator in the canonical DE may be instead by the orthogonal crossover operator without crossover rate. The performance of ccDE is widely evaluated on a set of 14 bound constrained numerical optimization problems compared with the canonical DE and several DE variants. The simulation results show that ccDE has better performances in terms of convergence rate and solution accuracy than other optimizers.

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