A probabilistic simplified sine cosine crow search algorithm for global optimization problems

Crow Search Algorithm (CSA) is a novel meta-heuristic optimizer that is based on the intelligent behavior of crows. There is rather simple with two adjustable parameters only, which in turn makes it very attractive for applications in different engineering areas. To compensate for the blindness of the location update perceived in CSA when being tracked, this paper introduces a probability simplified sine cosine algorithm to form a new hybrid algorithm called PSCCSA (Probabilistic Simplified Sine Cosine Crow Search Algorithm). In 16 well-known standard test functions, the proposed algorithm was compared with 5 meta-heuristic algorithms for evaluating the effectiveness of the algorithms (Crow Search Algorithm, standard Sine Cosine Algorithm, Probability Simplified Sine Cosine Algorithm, Multi-Verse Optimizer and Particle Swarm Optimization). In addition, PSCCSA has also been used to solve four classic engineering problems (pressure vessel design, speed reducer design, welded beam design and tension/compression spring design problem). The results show that the proposed algorithm is feasible and effective.

Automated summary

The Crow Search Algorithm is a new meta-heuristic optimizer inspired by the intelligent behavior of crows, and it has great potential for applications in engineering. This paper introduces a hybrid algorithm called PSCCSA, which combines the Crow Search Algorithm with a probability simplified sine cosine algorithm to overcome the limitations of blind location updates in CSA. The results of comparing the proposed algorithm with five other meta-heuristic algorithms and applying it to four classic engineering problems demonstrate its feasibility and effectiveness.