Hybrid improved sine cosine algorithm for mixed-integer nonlinear programming problems

Aiming at the shortcomings of existing algorithms in solving mixed-integer programming problems, such as local convergence and poor solution accuracy, this paper presents a hybrid improved sine and cosine algorithm. The improved position update formula is introduced to enhance the global and local s...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 27; číslo 20; s. 14909 - 14933
Hlavní autoři: Song, Haohao, Wang, Jiquan, Cheng, Zhiwen, Chang, Tiezhu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2023
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Computational Intelligence
Control
Convergence
Engineering
Evolution & development
Genetic algorithms
Integer programming
Mathematical Logic and Foundations
Mechatronics
Mixed integer
Mutation
Nonlinear programming
Optimization
Optimization algorithms
Robotics
Scheduling
Trigonometric functions
Water waves
Mixed-integer nonlinear programming
Improved sine cosine algorithm
Combined mutation
ISSN:1432-7643, 1433-7479
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Shrnutí:Aiming at the shortcomings of existing algorithms in solving mixed-integer programming problems, such as local convergence and poor solution accuracy, this paper presents a hybrid improved sine and cosine algorithm. The improved position update formula is introduced to enhance the global and local search abilities of the algorithm, and combined mutation is further given to avoid local convergence. The performance of proposed algorithm is validated by the high-dimensional modified CEC 2017 COPs and two complex engineering optimization problems. The experimental findings indicate that the hybrid mechanism of improved sine and cosine algorithm and combined mutation is demonstrably effective, and the proposed algorithm provides a satisfactory solution for addressing high-dimensional complicated mixed-integer programming problems.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08578-y