State Transition Simulated Annealing Algorithm for Discrete-Continuous Optimization Problems

A simulated annealing (SA) algorithm is an effective method for solving optimization problems, especially for combinatorial optimization problems. However, SA algorithms rely heavily on the iterative mechanism of the neighborhood structure. When the scale of the problem becomes larger, the convergen...

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Bibliographic Details
Published in:IEEE access Vol. 7; pp. 44391 - 44403
Main Authors: Han, Xiaoxia, Dong, Yingchao, Yue, Lin, Xu, Quanxi
Format: Journal Article
Language:English
Published: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Combinatorial analysis
combinatorial optimization
Continuous annealing
continuous optimization problem
Convergence
Genetic algorithms
Heuristic algorithms
Operators (mathematics)
Optimization
Search problems
Simulated annealing
simulated annealing algorithm
State transition
Traveling salesman problem
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:A simulated annealing (SA) algorithm is an effective method for solving optimization problems, especially for combinatorial optimization problems. However, SA algorithms rely heavily on the iterative mechanism of the neighborhood structure. When the scale of the problem becomes larger, the convergence rate is slow and it is difficult to reach the global optimal solution. In this paper, seven special intelligent operators are introduced to a SA algorithm, and therefore, a new state transition SA (STASA) algorithm is proposed for combinatorial optimization and continuous optimization problems. The performance of the STASA algorithm is tested on the traveling salesman problem and some continuous function optimization problems. The experimental results show that the proposed algorithm is efficient and reliable.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2908961