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

doi:10.1007/s00366-021-01578-2

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

2022-01-18

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Y. Subba Rao

Dengxu He *

Liangdong Qu

DOI：10.1007/s00366-021-01578-2

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Abstract

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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.

Keywords:

Crow search algorithm

Probabilistic simplified

Sine cosine algorithm

Global optimization

Engineering optimization