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37th Chinese Control Conference (CCC)

作者： Zhang, Chunmei Yang, Haibin Li, Jiajun Taiyuan Univ Sci & Technol Coll Elect & Informat Engn Taiyuan 030024 Shanxi Peoples R China

ISBN: (纸本)9789881563958

in order to solve the motor train-sets scheduling problem, a hybrid discrete differential evolution algorithm is proposed. The mathematical model is established for minimizing the total continuous time of one motor train-sets station. A hybrid algorithm of discrete differential evolution with scatter search is submitted to solve the motor train-sets scheduling problem. With regard to the initial population, more advanced designs derive from scatter search. Diversification generation method generates trial solutions. improvement method transforms a trial solution into one or more enhanced trial solutions. In discrete differential evolution, new differential mutant and crossover operators are designed based on integer permutation. The performance of the proposed hybrid algorithm is tested on the model of the train-sets scheduling, which shows that it outperforms the compared algorithm. The optimal scheduling scheme can be obtained effectively to achieve the minimum total continuity time.

关键词： discrete differential evolution algorithm Scatter Search Motor Train-sets Scheduling

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A discrete hybrid differential evolution algorithm for solving integer programming problems

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ENGINEERING OPTIMIZATION 2014年第9期46卷 1238-1268页

作者： Li, Hong Zhang, Li Xidian Univ Dept Math Sch Sci Xian 710071 Peoples R China

differential evolution (DE) is one of the most prominent new evolutionary algorithms for solving real-valued optimization problems. In this article, a discrete hybrid differential evolution algorithm is developed for solving global numerical optimization problems with discrete variables. Orthogonal crossover is combined with DE crossover to achieve crossover operation, and the simplified quadratic interpolation (SQI) method is employed to improve the algorithm's local search ability. A mixed truncation procedure is incorporated in the operations of DE mutation and SQI to ensure that the integer restriction is satisfied. Numerical experiments on 40 test problems including seventeen large-scale problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.

关键词： discrete differential evolution algorithm discrete global optimization orthogonal crossover integer programming

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SELECTIVE ENSEMBLE USING discrete differential evolution algorithm FOR SHORT-TERM LOAD FORECASTING

SELECTIVE ENSEMBLE USING DISCRETE DIFFERENTIAL EVOLUTION ALG...

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： YAN LI DONG-FENG WANG PU HAN School Of Control Science And Engineering North China Electric Power University Baoding 071003 China

In order to further improve the accuracy of short-term load forecasting, a selective neural network ensemble method using discrete differential evolution algorithm is proposed. Firstly, the individual vectors in differential evolution algorithm are dispersed. Secondly, a group of RBF neural networks with larger difference are trained independently and a binary bit string in multi-dimensional space with the value of 0 or 1 is used to describe all the possible neural network integrations. Lastly, part of individual networks is optimized selected to ensemble and an entropy method is used to determine the integrated weighted coefficient of component neural networks according to the variability of prediction error sequences. The experiments show that the proposed approach has higher accuracy and stability.

关键词： discrete differential evolution algorithm RBF neural network Selective ensemble Entropy weighted method Short-term load forecasting

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Hybrid discrete differential evolution algorithm for Motor Train-sets Scheduling

Hybrid Discrete Differential Evolution Algorithm for Motor T...

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第37届中国控制会议

作者： Chunmei Zhang Haibin Yang Jiajun Li College of Electronic and Information Engineering Taiyuan University of Science and Technology

In order to solve the motor train-sets scheduling problem,a hybrid discrete differential evolution algorithm is *** mathematical model is established for minimizing the total continuous time of one motor train-sets station.A hybrid algorithm of discrete differential evolution with scatter search is submitted to solve the motor train-sets scheduling *** regard to the initial population,more advanced designs derive from scatter *** generation method generates trial *** method transforms a trial solution into one or more enhanced trial *** discrete differential evolution,new differential mutant and crossover operators are designed based on integer *** performance of the proposed hybrid algorithm is tested on the model of the train-sets scheduling,which shows that it outperforms the compared *** optimal scheduling scheme can be obtained effectively to achieve the minimum total continuity time.

关键词： discrete differential evolution algorithm Scatter Search Motor Train-sets Scheduling

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An ensemble of discrete differential evolution algorithms for solving the generalized traveling salesman problem

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APPLIED MATHEMATICS AND COMPUTATION 2010年第9期215卷 3356-3368页

作者： Tasgetiren, M. Fatih Suganthan, P. N. Pan, Quan-Ke Yasar Univ Dept Ind Engn Izmir Turkey Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Liaocheng Univ Coll Comp Sci Liaocheng Shandong Peoples R China

In this paper, an ensemble of discrete differential evolution algorithms with parallel populations is presented. In a single populated discrete differential evolution (DDE) algorithm, the destruction and construction (DC) procedure is employed to generate the mutant population whereas the trial population is obtained through a crossover operator. The performance of the DDE algorithm is substantially affected by the parameters of DC procedure as well as the choice of crossover operator. In order to enable the DDE algorithm to make use of different parameter values and crossover operators simultaneously, we propose an ensemble of DDE (eDDE) algorithms where each parameter set and crossover operator is assigned to one of the parallel populations. Each parallel parent population does not only compete with offspring population generated by its own population but also the offspring populations generated by all other parallel populations which use different parameter settings and crossover operators. As an application area, the well-known generalized traveling salesman problem (GTSP) is chosen, where the set of nodes is divided into clusters so that the objective is to find a tour with minimum cost passing through exactly one node from each cluster. The experimental results show that none of the single populated variants was effective in solving all the GTSP instances whereas the eDDE performed substantially better than the single populated variants on a set of problem instances. Furthermore, through the experimental analysis of results, the performance of the eDDE algorithm is also compared against the best performing algorithms from the literature. Ultimately, all of the best known averaged solutions for larger instances are further improved by the eDDE algorithm. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Generalized traveling salesman problem Metaheuristic discrete differential evolution algorithm Ensemble of optimization algorithms evolutionary algorithms

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A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems

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COMPUTERS & OPERATIONS RESEARCH 2009年第8期36卷 2498-2511页

作者： Pan, Quan-Ke Wang, Ling Qian, Bin Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China

This paper presents a novel discrete differential evolution (DDE) algorithm for solving the no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, the individuals in the DDE algorithm are represented as discrete job permutations, and new mutation and crossover operators are developed based on this representation. Second, an elaborate one-to-one selection operator is designed by taking into account the domination status of a trial individual with its counterpart target individual as well as an archive set of the non-dominated solutions found so far. Third, a simple but effective local search algorithm is developed to incorporate into the DDE algorithm to stress the balance between global exploration and local exploitation. In addition, to improve the efficiency of the scheduling algorithm, several speed-up methods are devised to evaluate a job permutation and its whole insert neighborhood as well as to decide the domination status of a solution with the archive set. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed DDE algorithm is superior to a recently published hybrid differential evolution (HDE) algorithm [Qian B, Wang I, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Computers & Operations Research 2009:36(1):209-33] and the well-known multi-objective genetic local search algorithm (IMMOGLS2) [Ishibuchi H, Yoshida I, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on evolutionary Computation 2003;7(2):204-23] in terms of searching quality, diversity level, robustness and efficiency. Moreover, the effectiveness of incorporating the local search into the DDE algorithm is also investigated. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： differential evolution discrete differential evolution algorithm No-wait flow shop Multi-objective Insert neighborhood Speed-up

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discrete differential evolution for Text Summarization 13

Discrete Differential Evolution for Text Summarization

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International Conference on Information Technology (ICIT)

作者： Karwa, Shweta Chatterjee, Niladri Indian Inst Technol Delhi Dept Math New Delhi India

ISBN: (纸本)9781479980840

The paper proposes a modified version of differential evolution (DE) algorithm and optimization criterion function for extractive text summarization applications. Cosine Similarity measure has been used to cluster similar sentences based on a proposed criterion function designed for the text summarization problem, and important sentences from each cluster are selected to generate a summary of the document. The modified differential evolution model ensures integer state values and hence expedites the optimization as compared to conventional DE approach. Experiments showed a 95.5% improvement in time in the discrete DE approach over the conventional DE approach, while the precision and recall of extracted summaries remained comparable in all cases.

关键词： evolutionary computation text analysis DE algorithm cosine similarity measure differential evolution model discrete differential evolution algorithm document summary extractive text summarization applications integer state values optimization criterion function text summarization problem Biological cells Clustering algorithms evolutionary computation Optimization Sociology Statistics Vectors evolutionary algorithm text processing cytology Clustering algorithms Sociology similar measure criterion function MT texts

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A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops

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INFORMATION SCIENCES 2011年第16期181卷 3459-3475页

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. N. Chen, Angela H-L Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Yasar Univ Dept Ind Engn Izmir Turkey Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Nanya Inst Technol Dept Finance Tao Yuan 320 Taiwan

Obtaining an optimal solution for a permutation flowshop scheduling problem with the total flowtime criterion in a reasonable computational timeframe using traditional approaches and optimization tools has been a challenge. This paper presents a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime. Iterated greedy algorithms are comprised of local search procedures based on insertion and swap neighborhood structures. In the same context, we also consider a discrete differential evolution algorithm from our previous work. The performance of the proposed algorithms is tested on the well-known benchmark suite of Taillard. The highly effective performance of the discrete artificial bee colony and hybrid differential evolution algorithms is compared against the best performing algorithms from the existing literature in terms of both solution quality and CPU times. Ultimately, 44 out of the 90 best known solutions provided very recently by the best performing estimation of distribution and genetic local search algorithms are further improved by the proposed algorithms with short-term searches. The solutions known to be the best to date are reported for the benchmark suite of Taillard with long-term searches, as well. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Permutation flowshop scheduling problem Iterated greedy algorithm discrete differential evolution algorithm discrete artificial bee colony algorithm Estimation of distribution algorithm Genetic local search

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Permutation flow-shop scheduling problem to optimize a quadratic objective function

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ENGINEERING OPTIMIZATION 2017年第9期49卷 1589-1603页

作者： Ren, Tao Zhao, Peng Zhang, Da Liu, Bingqian Yuan, Huawei Bai, Danyu Northeastern Univ Software Coll Shenyang Peoples R China Beijing Gen Res Inst Min & Met Subinst Min Engn Beijing Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Shenyang Univ Chem Technol Sch Econ & Management Shenyang Peoples R China

A flow-shop scheduling model enables appropriate sequencing for each job and for processing on a set of machines in compliance with identical processing orders. The objective is to achieve a feasible schedule for optimizing a given criterion. Permutation is a special setting of the model in which the processing order of the jobs on the machines is identical for each subsequent step of processing. This article addresses the permutation flow-shop scheduling problem to minimize the criterion of total weighted quadratic completion time. With a probability hypothesis, the asymptotic optimality of the weighted shortest processing time schedule under a consistency condition (WSPT-CC) is proven for sufficiently large-scale problems. However, the worst case performance ratio of the WSPT-CC schedule is the square of the number of machines in certain situations. A discrete differential evolution algorithm, where a new crossover method with multiple-point insertion is used to improve the final outcome, is presented to obtain high-quality solutions for moderate-scale problems. A sequence-independent lower bound is designed for pruning in a branch-and-bound algorithm for small-scale problems. A set of random experiments demonstrates the performance of the lower bound and the effectiveness of the proposed algorithms.

关键词： Scheduling flow shop weighted quadratic completion time discrete differential evolution algorithm

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A discrete Artificial Bee Colony algorithm for the Permutation Flow Shop Scheduling Problem with Total Flowtime Criterion

A Discrete Artificial Bee Colony Algorithm for the Permutati...

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2010 IEEE World Congress on Computational Intelligence

作者： Tasgetiren, M. Fatih Pan, Quan-Ke Suganthan, P. Nagaratnam Chen, Angela H-L Yasar Univ Dept Ind Engn Izmir Turkey Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanya Inst Tech Dept Finance Chungli 320 Taiwan

ISBN: (纸本)9781424481262

Very recently, Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638-2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84-92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion, respectively. Both algorithms generated excellent results, thus improving all the best known solutions reported in the literature so far. However, in this paper, we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately, 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.

关键词： discrete artificial bee colony algorithm discrete differential evolution algorithm estimation distribution algorithm flow shop scheduling genetic algorithms greedy algorithms hybrid genetic local search algorithm insertion neighborhood structure iterated greedy search algorithm iterated local search algorithm iterative methods permutation flow shop scheduling search problems swap neighborhood structure total flowtime criterion variable neighborhood search procedure

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