Sine Cosine Crow Search Algorithm: A powerful hybrid meta heuristic for global optimization

TL;DR

This paper introduces the Sine Cosine Crow Search Algorithm, a hybrid metaheuristic combining CSA and SCA, which demonstrates superior performance on benchmark functions by balancing exploration and exploitation.

Contribution

The paper proposes a novel hybrid algorithm that integrates CSA and SCA, enhancing global optimization capabilities over existing metaheuristics.

Findings

Outperforms other metaheuristics on benchmark functions

Balances exploration and exploitation effectively

Provides competitive solutions in global optimization

Abstract

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.