Dynamic immune cooperative scheduling of agricultural machineries

Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutat...

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Vydáno v:Complex & intelligent systems Ročník 7; číslo 6; s. 2871 - 2884
Hlavní autoři: Liu, Xiaoyan, Zhu, Xinmeng, Hao, Kuangrong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.12.2021
Springer Nature B.V
Témata:
Agricultural equipment
Algorithms
Complexity
Computational Intelligence
Data Structures and Information Theory
Engineering
Mutation
Optimization
Original Article
Scheduling
Search algorithms
Search methods
Tabu search
Multi-objective immune algorithm
Self-adapted mutation operator
Non-dominated neighbor-based selection
Scheduling of agricultural machineries
Tabu search algorithm
ISSN:2199-4536, 2198-6053
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Abstract Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.
AbstractList Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.
Author Hao, Kuangrong
Liu, Xiaoyan
Zhu, Xinmeng
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CitedBy_id crossref_primary_10_1145_3641290
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crossref_primary_10_3390_agriengineering7060197
crossref_primary_10_1016_j_compag_2024_109121
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Keywords Multi-objective immune algorithm
Self-adapted mutation operator
Non-dominated neighbor-based selection
Scheduling of agricultural machineries
Tabu search algorithm
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Snippet Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based...
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SubjectTerms Agricultural equipment
Algorithms
Complexity
Computational Intelligence
Data Structures and Information Theory
Engineering
Mutation
Optimization
Original Article
Scheduling
Search algorithms
Search methods
Tabu search
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