In order to achieve the research goal of solving the problems of accuracy, recall and low F1 value of traditional online education resources personalised recommendation methods, a new personalised recommendation metho...
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In order to achieve the research goal of solving the problems of accuracy, recall and low F1 value of traditional online education resources personalised recommendation methods, a new personalised recommendation method of online education resources under the background of teaching reform was designed. Using the BERT model to extract learner preference vectors and feature vectors of online educational resources, an improved discrete differential evolution algorithm is designed, which is used to recall and sort online educational resource sequences. Combined with the collaborative filtering algorithm to generate recommendation sequences, personalised recommendation results of online education resources are obtained. Simulation experiments show that the accuracy rate curve of this paper method relatively flat, accuracy rate is always above 93%, the maximum recall rate is 98%, and the F1 mean is 9.67, the recommended results are reliable.
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 opti...
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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.
This paper is concerned with solving the single machine total weighted tardiness problem with sequence dependent setup times by a discrete differential evolution algorithm developed by the authors recently. Its perfor...
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This paper is concerned with solving the single machine total weighted tardiness problem with sequence dependent setup times by a discrete differential evolution algorithm developed by the authors recently. Its performance is enhanced by employing different population initialization schemes based on some constructive heuristics such as the well-known NEH and the greedy randomized adaptive search procedure (GRASP) as well as some priority rules such as the earliest weighted due date (EWDD) and the apparent tardiness cost with setups (ATCS). Additional performance enhancement is further achieved by the inclusion of a referenced local search (RLS) in the algorithm together with the use of destruction and construction (DC) procedure when obtaining the mutant population. Furthermore, to facilitate the greedy job insertion into a partial solution which will be employed in the NEH. GRASP, DC heuristics as well as in the RLS local search, some newly designed speed-up methods are presented for the insertion move for the first time in the literature. They are novel contributions of this paper to the single machine tardiness related scheduling problems with sequence dependent setup times. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, its highly effective performance with substantial margins both in solution quality and CPU time is shown against the best performing algorithms from the literature, in particular, against the very recent newly designed particle swarm and ant colony optimization algorithms of Anghinolfi and Paolucci [A new discrete particle swarm optimization approach for the single machine total weighted tardiness scheduling problem with sequence dependent setup times. European journal of Operational Research 2007;doi:10.1016/***.2007.10.044] and Anghinolfi and Paolucci [A new ant colony optimization approach for the single m
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 diffe...
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ISBN:
(纸本)9781424447053
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 differentialevolutionalgorithm 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.
The paper proposes a modified version of differentialevolution (DE) algorithm and optimization criterion function for extractive text summarization applications. Cosine Similarity measure has been used to cluster sim...
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ISBN:
(纸本)9781479980840
The paper proposes a modified version of differentialevolution (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 differentialevolution 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.
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 diffe...
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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 differentialevolutionalgorithm 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.
Very recently, Pan et al. [Proceedings of the 9th Annual Conference on Genetic and evolutionary Computation, GECC007, pp. 126-33] presented a new and novel discrete differential evolution algorithm for the permutation...
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Very recently, Pan et al. [Proceedings of the 9th Annual Conference on Genetic and evolutionary Computation, GECC007, pp. 126-33] presented a new and novel discrete differential evolution algorithm for the permutation flowshop scheduling problem with the makespan criterion. On the other hand, the iterated greedy algorithm is proposed by [Ruiz, R.. & Stutzle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-49] for the permutation flowshop scheduling problem with the makespan criterion. However, both algorithms are not applied to the permutation flowshop scheduling problem with the total flowtime criterion. Based on their excellent performance with the makespan criterion, we extend both algorithms in this paper to the total flowtime objective. Furthermore, we propose a new and novel referenced local search procedure hybridized with both algorithms to further improve the solution quality. The referenced local search exploits the space based on reference positions taken from a reference solution in the hope of finding better positions for jobs when performing insertion operation. Computational results show that both algorithms with the referenced local search are either better or highly competitive to all the existing approaches in the literature for both objectives of makespan and total flowtime. Especially for the total flowtime criterion, their performance is superior to the particle swarm optimization algorithms proposed by [Tasgetiren, M. F., Liang, Y. -C., Sevkli, M., Gencyilmaz, G. (2007). Particle swarm optimization algorithm for makespan and total flowtime minimization in permutation flowshop sequencing problem. European journal of Operational Research, 177(3), 1930-47] and [Jarboui, B., Ibrahim, S., Siarry, P., Rebai, A. (2007). A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineeri
In this paper, an ensemble of discrete differential evolution algorithms with parallel populations is presented. In a single populated discretedifferentialevolution (DDE) algorithm, the destruction and construction ...
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In this paper, an ensemble of discrete differential evolution algorithms with parallel populations is presented. In a single populated discretedifferentialevolution (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.
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 chal...
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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 differentialevolutionalgorithms 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.
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 a...
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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 discretedifferentialevolution (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.
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