Improved sine cosine algorithm with crossover scheme for global optimization

Sine Cosine Algorithm is a recently developed algorithm based on the characteristics of sine and cosine trigonometric functions, to solve global optimization problems. This paper introduces a novel improved version of sine cosine algorithm, which enhances the exploitation ability of solutions and re...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Knowledge-based systems Ročník 165; s. 374 - 406
Hlavní autori: Gupta, Shubham, Deep, Kusum
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 01.02.2019
Elsevier Science Ltd
Predmet:
Algorithms
Benchmark tests
Benchmarks
Convergence
Crossovers
Engineering optimization problems
Experiments
Exploitation
Global optimization
Image segmentation
Multilevel thresholding
Optimization
Performance indices
Performance measurement
Population based algorithms
Robustness
Robustness (mathematics)
Segmentation
Self instruction
Sine cosine algorithm
Skills
Statistical tests
Trigonometric functions
Engineering optimization problems
Multilevel thresholding
Sine cosine algorithm
Optimization
Population based algorithms
ISSN:0950-7051, 1872-7409
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Sine Cosine Algorithm is a recently developed algorithm based on the characteristics of sine and cosine trigonometric functions, to solve global optimization problems. This paper introduces a novel improved version of sine cosine algorithm, which enhances the exploitation ability of solutions and reduces the overflow of diversity present in the search equations of classical SCA. The proposed algorithm is named as ISCA. The key feature in the proposed algorithm is the hybridization of exploitation skills of crossover with personal best state of individual solutions and integration of self-learning and global search mechanisms. To evaluate these skills in ISCA, a classical set of well-known benchmark problems, standard IEEE CEC 2014 benchmark test and a recent set of benchmark problems, IEEE CEC 2017 have been taken. Several performance metrics (such as convergence, statistical test, performance index), employed on ISCA, ensure the robustness and efficiency of the algorithm. In the paper, the proposed algorithm ISCA is also used to solve five well-known engineering optimization problems. At the end of the paper, the proposed algorithm is also used for multilevel thresholding in image segmentation. The numerical experiments and analysis demonstrate that the proposed algorithm (ISCA) can be highly effective in solving real-life optimization problems. •A new method called ISCA is proposed for global optimization problems.•The ISCA improves the SCA using crossover and personal best memory of agents.•The classical, CEC 2014 and CEC 2017 benchmarks are used to examine ISCA.•The ISCA is used for engineering problems and image thresholding problem.•Comparisons illustrate the improvement on the performance of ISCA.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.12.008