TY  - JOUR
T1  - Chaotic Sine-Cosine Optimization Algorithms
AU - Tahir and, Dunia S. AU - Ali, Ramzy S. 
JO  - International Journal of Soft Computing
VL  - 13
IS  - 3
SP  - 108
EP  - 122
PY  - 2018
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2018.108.122
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2018.108.122
KW  - Chaos
KW  -sine-cosine optimization algorithm
KW  -chaotic sine-cosine
KW  -optimization algorithms
KW  -differential
KW  -benchmarked
KW  -evolutional
AB  - A Sine-Cosine Algorithm (SCA) is a new metaheuristic optimization algorithm. Sine-Cosine Algorithm
(SCA) is inspired from the sine and cosine mathematical functions. The standard Sine-Cosine Algorithm (SCA)
has some problems, like any of the other techniques such as slow convergence and falling into local solutions.
To overcome these problems, this study suggested four different chaotic Sine-Cosine Algorithms (CSCAs)
methods. The random parameters in the standard Sine-Cosine Algorithm (SCA) are replaced with the chaotic
sequences to improve the performance of the standard algorithm. Five one dimensional various chaotic maps
are implemented.The proposed chaotic Sine-Cosine Algorithms (CSCAs) methods are benchmarked on ten test
benchmark functions. The statistical results showed that all chaotic Sine-Cosine Algorithms (CSCAs) methods
can be outperformed the standard Sine-Cosine Algorithm (SCA) for these benchmark functions and the
intermittency and circle maps are the best maps for boosting the performance of the first and fourth chaotic
CSCAs. While the Gauss map is the most suitable variant for the second and third chaotic CSCAs methods,
respectively. Additionally, the results proved that the fourth proposed algorithm with the circle map
significantly overtook on the other proposed algorithms. The effectiveness of all chaotic Sine-Cosine
Algorithms (CSCAs) methods are proved by comparing their results with the well-known metaheuristic methods
such as the standard Sine-Cosine Algorithm (SCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO)
and Differential Evolution (DE).
ER  -