Sine Cosine Crow Search Algorithm: A powerful hybrid meta heuristic for global optimization
This is an incremental improvement for researchers in optimization algorithms, offering a hybrid approach to balance exploration and exploitation.
The authors tackled global optimization by proposing a hybrid meta-heuristic algorithm (SCCSA) combining Crow Search Algorithm and Sine Cosine Algorithm, which achieved competitive performance on seven benchmark functions compared to state-of-the-art methods.
This paper presents a novel hybrid algorithm named Since Cosine Crow Search Algorithm. To propose the SCCSA, two novel algorithms are considered including Crow Search Algorithm (CSA) and Since Cosine Algorithm (SCA). The advantages of the two algorithms are considered and utilize to design an efficient hybrid algorithm which can perform significantly better in various benchmark functions. The combination of concept and operators of the two algorithms enable the SCCSA to make an appropriate trade-off between exploration and exploitation abilities of the algorithm. To evaluate the performance of the proposed SCCSA, seven well-known benchmark functions are utilized. The results indicated that the proposed hybrid algorithm is able to provide very competitive solution comparing to other state-of-the-art meta heuristics.