Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling
The optimal scheduling of multi-type combine harvesters is a crucial topic in improving the operating efficiency of combine harvesters. Due to the NP-hard property of this problem, developing appropriate optimization approaches is an intractable task. The multi-type combine harvesters scheduling pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/10/1/64 |
| _version_ | 1797492640050053120 |
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| author | Wenqiang Yang Zhile Yang Yonggang Chen Zhanlei Peng |
| author_facet | Wenqiang Yang Zhile Yang Yonggang Chen Zhanlei Peng |
| author_sort | Wenqiang Yang |
| collection | DOAJ |
| description | The optimal scheduling of multi-type combine harvesters is a crucial topic in improving the operating efficiency of combine harvesters. Due to the NP-hard property of this problem, developing appropriate optimization approaches is an intractable task. The multi-type combine harvesters scheduling problem considered in this paper deals with the question of how a given set of harvesting tasks should be assigned to each combine harvester, such that the total cost is comprehensively minimized. In this paper, a novel multi-type combine harvesters scheduling problem is first formulated as a constrained optimization problem. Then, a whale optimization algorithm (WOA) including an opposition-based learning search operator, adaptive convergence factor and heuristic mutation, namely, MWOA, is proposed and evaluated based on benchmark functions and comprehensive computational studies. Finally, the proposed intelligent approach is used to solve the multi-type combine harvesters scheduling problem. The experimental results prove the superiority of the MWOA in terms of solution quality and convergence speed both in the benchmark test and for solving the complex multi-type combine harvester scheduling problem. |
| first_indexed | 2024-03-10T01:05:36Z |
| format | Article |
| id | doaj.art-9595891f5ae5415c84a9cfabc39f735b |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T01:05:36Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-9595891f5ae5415c84a9cfabc39f735b2023-11-23T14:26:57ZengMDPI AGMachines2075-17022022-01-011016410.3390/machines10010064Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters SchedulingWenqiang Yang0Zhile Yang1Yonggang Chen2Zhanlei Peng3Postdoctoral Station, Henan University of Science and Technology, Luoyang 471000, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaPostdoctoral Station, Henan University of Science and Technology, Luoyang 471000, ChinaPostdoctoral Station, Henan University of Science and Technology, Luoyang 471000, ChinaThe optimal scheduling of multi-type combine harvesters is a crucial topic in improving the operating efficiency of combine harvesters. Due to the NP-hard property of this problem, developing appropriate optimization approaches is an intractable task. The multi-type combine harvesters scheduling problem considered in this paper deals with the question of how a given set of harvesting tasks should be assigned to each combine harvester, such that the total cost is comprehensively minimized. In this paper, a novel multi-type combine harvesters scheduling problem is first formulated as a constrained optimization problem. Then, a whale optimization algorithm (WOA) including an opposition-based learning search operator, adaptive convergence factor and heuristic mutation, namely, MWOA, is proposed and evaluated based on benchmark functions and comprehensive computational studies. Finally, the proposed intelligent approach is used to solve the multi-type combine harvesters scheduling problem. The experimental results prove the superiority of the MWOA in terms of solution quality and convergence speed both in the benchmark test and for solving the complex multi-type combine harvester scheduling problem.https://www.mdpi.com/2075-1702/10/1/64multi-type combine harvesters schedulingwhale optimization algorithmopposition-based learningadaptive convergence factorheuristic mutation |
| spellingShingle | Wenqiang Yang Zhile Yang Yonggang Chen Zhanlei Peng Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling Machines multi-type combine harvesters scheduling whale optimization algorithm opposition-based learning adaptive convergence factor heuristic mutation |
| title | Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling |
| title_full | Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling |
| title_fullStr | Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling |
| title_full_unstemmed | Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling |
| title_short | Modified Whale Optimization Algorithm for Multi-Type Combine Harvesters Scheduling |
| title_sort | modified whale optimization algorithm for multi type combine harvesters scheduling |
| topic | multi-type combine harvesters scheduling whale optimization algorithm opposition-based learning adaptive convergence factor heuristic mutation |
| url | https://www.mdpi.com/2075-1702/10/1/64 |
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