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Chen Z, Xinxian L, Guo R, Zhang L, Dhahbi S, Bourouis S, Liu L, Wang X. Dispersed differential hunger games search for high dimensional gene data feature selection. Comput Biol Med 2023; 163:107197. [PMID: 37390761 DOI: 10.1016/j.compbiomed.2023.107197] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/02/2023]
Abstract
The realms of modern medicine and biology have provided substantial data sets of genetic roots that exhibit a high dimensionality. Clinical practice and associated processes are primarily dependent on data-driven decision-making. However, the high dimensionality of the data in these domains increases the complexity and size of processing. It can be challenging to determine representative genes while reducing the data's dimensionality. A successful gene selection will serve to mitigate the computing costs and refine the accuracy of the classification by eliminating superfluous or duplicative features. To address this concern, this research suggests a wrapper gene selection approach based on the HGS, combined with a dispersed foraging strategy and a differential evolution strategy, to form a new algorithm named DDHGS. Introducing the DDHGS algorithm to the global optimization field and its binary derivative bDDHGS to the feature selection problem is anticipated to refine the existing search balance between explorative and exploitative cores. We assess and confirm the efficacy of our proposed method, DDHGS, by comparing it with DE and HGS combined with a single strategy, seven classic algorithms, and ten advanced algorithms on the IEEE CEC 2017 test suite. Furthermore, to further evaluate DDHGS' performance, we compare it with several CEC winners and DE-based techniques of great efficiency on 23 popular optimization functions and the IEEE CEC 2014 benchmark test suite. The experimentation asserted that the bDDHGS approach was able to surpass bHGS and a variety of existing methods when applied to fourteen feature selection datasets from the UCI repository. The metrics measured--classification accuracy, the number of selected features, fitness scores, and execution time--all showed marked improvements with the use of bDDHGS. Considering all results, it can be concluded that bDDHGS is an optimal optimizer and an effective feature selection tool in the wrapper mode.
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Affiliation(s)
- Zhiqing Chen
- School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou, 325035, China.
| | - Li Xinxian
- Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, China.
| | - Ran Guo
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Lejun Zhang
- Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, 510006, China; College of Information Engineering, Yangzhou University, Yangzhou, 225127, China; Research and Development Center for E-Learning, Ministry of Education, Beijing, 100039, China.
| | - Sami Dhahbi
- Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Muhayil, Aseer, 62529, Saudi Arabia.
| | - Sami Bourouis
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O.Box 11099, Taif, 21944, Saudi Arabia.
| | - Lei Liu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, 610065, China.
| | - Xianchuan Wang
- Information Technology Center, Wenzhou Medical University, Wenzhou, 325035, China.
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Duan J, Wang M, Zhang Q, Qin J. Distributed shop scheduling: A comprehensive review on classifications, models and algorithms. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:15265-15308. [PMID: 37679180 DOI: 10.3934/mbe.2023683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
In the intelligent manufacturing environment, modern industry is developing at a faster pace, and there is an urgent need for reasonable production scheduling to ensure an organized production order and a dependable production guarantee for enterprises. Additionally, production cooperation between enterprises and different branches of enterprises is increasingly common, and distributed manufacturing has become a prevalent production model. In light of these developments, this paper presents the research background and current state of distributed shop scheduling. It summarizes relevant research on issues that align with the new manufacturing model, explores hot topics and concerns and focuses on the classification of distributed parallel machine scheduling, distributed flow shop scheduling, distributed job shop scheduling and distributed assembly shop scheduling. The paper investigates these scheduling problems in terms of single-objective and multi-objective optimization, as well as processing constraints. It also summarizes the relevant optimization algorithms and their limitations. It also provides an overview of research methods and objects, highlighting the development of solution methods and research trends for new problems. Finally, the paper analyzes future research directions in this field.
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Affiliation(s)
- Jianguo Duan
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
| | - Mengting Wang
- School of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Qinglei Zhang
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
| | - Jiyun Qin
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
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Zhao F, Di S, Wang L. A Hyperheuristic With Q-Learning for the Multiobjective Energy-Efficient Distributed Blocking Flow Shop Scheduling Problem. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:3337-3350. [PMID: 35994539 DOI: 10.1109/tcyb.2022.3192112] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Carbon peaking and carbon neutrality, which are the significant national strategy for sustainable development, have attracted considerable attention from production enterprises. In this study, the energy consumption is considered in the distributed blocking flow shop scheduling problem (DBFSP). A hyperheuristic with Q -learning (HHQL) is presented to address the energy-efficient DBFSP (EEDBFSP). Q -learning is employed to select an appropriate low-level heuristic (LLH) from a predesigned LLH set according to historical information fed back by LLH. An initialization method, which considers both total tardiness (TTD) and total energy consumption (TEC), is proposed to construct the initial population. The ε -greedy strategy is introduced to utilize the learned knowledge while retaining a certain degree of exploration in the process of selecting LLH. The acceleration operation of the job on the critical path is designed to optimize TTD. The deceleration operation of the job on the noncritical path is designed to optimize TEC. The statistical and computational experimentation in an extensive benchmark testified that the HHQL outperforms the other comparison algorithm regarding efficiency and significance in solving EEDBFSP.
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A Knowledge-Based Cooperative Differential Evolution Algorithm for Energy-Efficient Distributed Hybrid Flow-Shop Rescheduling Problem. Processes (Basel) 2023. [DOI: 10.3390/pr11030755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
Due to the increasing level of customization and globalization of competition, rescheduling for distributed manufacturing is receiving more attention. In the meantime, environmentally friendly production is becoming a force to be reckoned with in intelligent manufacturing industries. In this paper, the energy-efficient distributed hybrid flow-shop rescheduling problem (EDHFRP) is addressed and a knowledge-based cooperative differential evolution (KCDE) algorithm is proposed to minimize the makespan of both original and newly arrived orders and total energy consumption (simultaneously). First, two heuristics were designed and used cooperatively for initialization. Next, a three-dimensional knowledge base was employed to record the information carried out by elite individuals. A novel DE with three different mutation strategies is proposed to generate the offspring. A local intensification strategy was used for further enhancement of the exploitation ability. The effects of major parameters were investigated and extensive experiments were carried out. The numerical results prove the effectiveness of each specially-designed strategy, while the comparisons with four existing algorithms demonstrate the efficiency of KCDE in solving EDHFRP.
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Zhang W, Zhang X, Hao X, Gen M, Zhang G, Yang W. Multi-stage hybrid evolutionary algorithm for multiobjective distributed fuzzy flow-shop scheduling problem. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4838-4864. [PMID: 36896525 DOI: 10.3934/mbe.2023224] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of fuzzy completion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages. In the first stage, the hybrid sampling strategy makes the population rapidly converge toward the Pareto front (PF) in multiple directions. In the second stage, the sequence difference-based differential evolution (SDDE) is used to speed up the convergence speed to improve the convergence performance. In the last stage, the evolutional direction of SDDE is changed to guide individuals to search the local area of the PF, thereby further improving the convergence and distribution performance. The results of experiments show that the performance of MSHEA-SDDE is superior to the classical comparison algorithms in terms of solving the DFFSP.
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Affiliation(s)
- Wenqiang Zhang
- College of Information Science and Engineering, Henan University of Technology, China
| | - Xiaoxiao Zhang
- College of Information Science and Engineering, Henan University of Technology, China
| | - Xinchang Hao
- School of Art and Design, Changzhou Institute of Technology, China
| | - Mitsuo Gen
- Fuzzy Logic Systems Institute, Tokyo University of Science, Japan
| | - Guohui Zhang
- School of Management Engineering, Zhengzhou University of Aeronautics, China
| | - Weidong Yang
- Henan Key Laboratory of Grain Photoelectric Detection and Control, Henan University of Technology, China
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Yi T, Li M, Lei D. A shuffled frog-leaping algorithm with Q-learning for unrelated parallel machine scheduling with additional resource and learning effect. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Unrelated parallel machine scheduling problem (UPMSP) with additional resources and UPMSP with learning effect have attracted some attention; however, UPMSP with additional resources and learning effect is seldom studied and meta-heuristics for UPMSP hardly possess reinforcement learning as new optimization mechanism. In this study, a shuffled frog-leaping algorithm with Q-learning (QSFLA) is presented to solve UPMSP with one additional resource and learning effect. A new solution presentation is presented. Two populations are obtained by division. A Q-learning algorithm is constructed to dynamically decide search operator and search times. It has 12 states depicted by population quality evaluation, four actions defined as search operators, a new reward function and a new action selection. Extensive experiments are conducted. Computational results demonstrate that QSFLA has promising advantages for the considered UPMSP.
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Affiliation(s)
- Tian Yi
- School of Automation, Wuhan University of Technology, Wuhan, China
| | - Mingbo Li
- School of Automation, Wuhan University of Technology, Wuhan, China
| | - Deming Lei
- School of Automation, Wuhan University of Technology, Wuhan, China
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Chen S, Pan QK, Gao L, Miao ZH, Peng C. Energy-efficient distributed heterogeneous blocking flowshop scheduling problem using a knowledge-based iterated Pareto greedy algorithm. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08012-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Ɖurasević M, Jakobović D. Heuristic and metaheuristic methods for the parallel unrelated machines scheduling problem: a survey. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10247-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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An effective metaheuristic with a differential flight strategy for the distributed permutation flowshop scheduling problem with sequence-dependent setup times. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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A hash map-based memetic algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance to minimize total flowtime. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108413] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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