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Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times

ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2021年 97卷 104016-104016页

作者： Huang, Jiang-Ping Pan, Quan-Ke Miao, Zhong-Hua Gao, Liang Shanghai Univ Sch Mech Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Shandong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The distributed permutation flowshop scheduling problem (DPFSP) has been a hot issue in recent years. Due to the practical relevance of sequence-dependent setup time (SDST), we consider the DPFSP with SDST to minimize makespan. For the purpose, we propose three constructive heuristics and an effective discrete artificial bee colony (DABC) algorithm. All the heuristics are based on a greedy assignment rule and a local search of job blocks in each factory. In the local search, three different setup times are respectively considered for inserting a job block. In the DABC, to balance the local exploitation and the global exploration, we propose six composite neighborhood operators according to the problem characteristics. The first three are based on insertion and swap operators, and the second three have a close relationship with the critical factory. A problem-oriented local search method is developed to improve the best individual in the population. A comprehensive computational campaign against the closely related and state-of-the-art algorithms in the literature shows that both the proposed heuristics and DABC are very effective for solving the problem under consideration.

关键词： Flowshop Scheduling Heuristics discrete artificial bee colony algorithm Meta-heuristics

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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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Apply the discrete artificial bee colony algorithm to the blocking flow shop problem with makespan criterion

Apply the discrete artificial bee colony algorithm to the bl...

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23rd Chinese Control and Decision Conference

作者： Han, Yu-Yan Duan, Jun-Hua Zhang, Min Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China

ISBN: (纸本)9781424487363

This paper applies the discrete artificial bee colony (DABC) algorithm to the blocking flow shop (BFS) scheduling problem with makespan criterion. The DABC algorithm utilizes discrete job permutations to represent food sources and applies discrete operators to generate new food sources for the employed bees, onlookers and scouts. An initialization scheme based on the variant of MM heuristic is presented to construct an initial population with a certain level of quality and diversity. The DABC algorithm hybrids a local search based on the insert neighborhood to improve the algorithm's local exploitation ability. The computational results demonstrate that the DABC algorithm is effective and efficient for the blocking flow shop scheduling problems with makespan criterion.

关键词： discrete artificial bee colony algorithm blocking flow shop scheduling local search

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Feature selection of Complex Power Quality Disturbances and Parameter Optimization of Random Forest 4

Feature selection of Complex Power Quality Disturbances and ...

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4th International Conference on Intelligent Green Building and Smart Grid (IGBSG)

作者： Wang, Renming Wang, Hongyang Wang, Lingyun China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China

ISBN: (纸本)9781728121482

A hybrid method based on modified discrete artificial bee colony algorithm(MDABC) for power quality disturbance(PQD) signal feature selection and parameter optimization of random forest(RF) is proposed. Firstly, time-frequency features of the complex power quality disturbance signal are extracted using s-transform(ST) to form an original feature set;Then, by using default parameters of RF, the out-of-bag(OOB) Permutation test value of each feature in the original feature set is calculated as the feature weight, and the features of the training and verification data set are rearranged in descending order accordingly. Finally, taking the generalization error of RF as the objective function, the forest scale: nTree, the number of input features:Ni and the node feature subset size:q are optimized using MDABC to determine the optimal parameters of RF and the optimal feature set. It can be seen from the experiment that compared with the RF classifier before optimization, the accuracy of the MDABC-RF classifier in the classification of 16 and 19 complex PQD signals is better, and its operating efficiency is greatly improved.

关键词： power quality discrete artificial bee colony algorithm random forest parameter optimization feature selection

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Apply the discrete artificial bee colony algorithm to the blocking flow shop problem with makespan criterion

Apply the discrete artificial bee colony algorithm to the bl...

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2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Yu-Yan Han Jun-Hua Duan Min Zhang School of Computer Science Liaocheng University China 252059

This paper applies the discrete artificial bee colony (DABC) algorithm to the blocking flow shop (BFS) scheduling problem with makespan criterion. The DABC algorithm utilizes discrete job permutations to represent food sources and applies discrete operators to generate new food sources for the employed bees, onlookers and scouts. An initialization scheme based on the variant of MM heuristic is presented to construct an initial population with a certain level of quality and diversity. The DABC algorithm hybrids a local search based on the insert neighborhood to improve the algorithm’s local exploitation ability. The computational results demonstrate that the DABC algorithm is effective and efficient for the blocking flow shop scheduling problems with makespan criterion.

关键词： discrete artificial bee colony algorithm blocking flow shop scheduling local search

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CA Joint Resource Allocation algorithm Based on QoE Weight

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第5期12卷 2233-2252页

作者： Liu Jun-Xia Jia Zhen-Hong Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Xinjiang Peoples R China Xinjiang Inst Engn Dept Elect & Informat Urumqi 830023 Xinjiang Peoples R China

For the problem of cross-layer joint resource allocation (JRA) in the Long-Term Evolution (LTE)-Advanced standard using carrier aggregation (CA) technology, it is difficult to obtain the optimal resource allocation scheme. This paper proposes a joint resource allocation algorithm based on the weights of user's average quality of experience (JRA-WQOE). In contrast to prevalent algorithms, the proposed method can satisfy the carrier aggregation abilities of different users and consider user fairness. An optimization model is established by considering the user quality of experience (QoE) with the aim of maximizing the total user rate. In this model, user QoE is quantified by the mean opinion score (MOS) model, where the average MOS value of users is defined as the weight factor of the optimization model. The JRA-WQOE algorithm consists of the iteration of two algorithms, a component carrier (CC) and resource block (RB) allocation algorithm called DABC-CCRBA and a subgradient power allocation algorithm called SPA. The former is used to dynamically allocate CC and RB for users with different carrier aggregation capacities, and the latter, which is based on the Lagrangian dual method, is used to optimize the power allocation process. Simulation results showed that the proposed JRA-WQOE algorithm has low computational complexity and fast convergence. Compared with existing algorithms, it affords obvious advantages such as improving the average throughput and fairness to users. With varying numbers of users and signal-to-noise ratios (SNRs), the proposed algorithm achieved higher average QoE values than prevalent algorithms.

关键词： LTE-Advanced carrier aggregation resource allocation quality of experience discrete artificial bee colony algorithm

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An effective computational approach based on XFEM and a novel three-step detection algorithm for multiple complex flaw clusters

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COMPUTERS & STRUCTURES 2017年第Dec.期193卷 207-225页

作者： Ma, Chunping Yu, Tiantang Le Van Lich Tinh Quoc Bui Hohai Univ Dept Engn Mech Nanjing 211100 Jiangsu Peoples R China Kyoto Univ Dept Mech Engn & Sci Nishikyo Ku Kyoto 6158540 Japan Duy Tan Univ Inst Res & Dev Da Nang City Vietnam Tokyo Inst Technol Dept Civil & Environm Engn Meguro Ku 2-12-1-W8-22 Ookayama Tokyo 1528552 Japan

This paper presents an effective computational approach comprised of forward and inverse analyses for detection of multiple complex flaw clusters in elastic solids. A three-step detection strategy is introduced for inverse analysis, whereas extended finite element method (XFEM) is adopted for forward analysis. The use of XFEM is to avoid re-meshing during the change of flaw geometries. The three-step detection strategy involves: firstly, an optimization method that couples an improved discrete artificial bee colony algorithm and hierarchical clustering analysis (IDABC-HCA) is used to capture subdomains containing flaws with limited measure points in the global domain;secondly, additional measure points are introduced locally within each captured subdomain, where the number of flaws and the rough geometry of each flaw are quickly determined with the IDABC-HCA;finally, true geometries of flaws are obtained on the basis of the rough geometries by the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. To save computational time, "Queue and Kill" method is proposed to actively identify and eliminate the improper candidate flaws and/or flaw clusters. Three numerical examples of multiple flaw detection that include simple and complex flaw geometries are analyzed. The results demonstrate that the proposed approach can effectively detect multiple complex flaw clusters without prior information of the flaw number. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Flaw clusters Inverse problem XFEM discrete artificial bee colony algorithm Hierarchical clustering analysis BFGS

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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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Facial Recognition Using discrete ABC 5

Facial Recognition Using Discrete ABC

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Kallianpur, Akshay K. Kalyani, Aditya Naligay, Subramanya E. Kallimani, Jagadish S. MS Ramaiah Inst Technol Bangalore Karnataka India

ISBN: (纸本)9781509020287

Facial recognition is a topic of interest for research as it has room for improvement in the accuracy of the recognition rate. To achieve this, either the recognition algorithm is modified or more efficient pre-processing techniques are used. This paper proposes a novel and optimized artificial bee colony (ABC) algorithm, to perform facial recognition. Although the database being used here is Labeled Faces in the Wild (LFW), it is also tested on Carnigie Melon database to ensure consistent results. Applying the concept to perform facial recognition and obtaining satisfactory results is sublime. The discretization of the ABC algorithm serves many applications in the fields of pattern recognition and image analysis. This version of the ABC algorithm contains certain elements from Particle Swarm Optimization (PSO), hence yielding a hybrid algorithm that contains the best of both its contributors. This paper primarily focuses on the application of the proposed technique onto facial recognition. Standard data sets are used to test and quantify the efficacy of the algorithm. Given that the requirement is an extremely swift pattern recognition software that does not compromising the efficiency of the recognition rate, the proposed algorithm upholds both these criteria and is a robust technique.

关键词： discrete artificial bee colony algorithm Gaussian Filters Laplacian Filters Feature Extraction discrete Cosine Transform

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Facial Recognition Using discrete ABC

Facial Recognition Using Discrete ABC

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International Conference on Advances in Computing, Communications and Informatics

作者： Akshay K. Kallianpur Aditya Kalyani Subramanya E. Naligay Jagadish S. Kallimani M.S. Ramaiah Institute of Technology Bangalore Karnataka India

ISBN: (纸本)9781509020300

关键词： discrete artificial bee colony algorithm Gaussian Filters Laplacian Filters Feature Extraction discrete Cosine Transform

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