Abstract
The crow search algorithm (CSA) is a new intelligent optimization algorithm based on the behavior of the crow population, which has been proven to perform well. However, its simple search mechanism also leads to the algorithm's slow convergence speed and its ease of falling into local optimization when solving complex optimization problems. In order to overcome these problems, this paper proposes an improved CSA (ISCSA) based on a spiral search mechanism. By introducing a weight coefficient, an optimal guidance position and a spiral search mechanism, the position equation was updated to accelerate the convergence of the algorithm and make the exploration and exploitation of CSA more balanced. Meanwhile, adding Gaussian variation and random perturbation strategy made it difficult for the algorithm to fall into local optimization. The advantages of the proposed ISCSA were evaluated using 23 benchmark functions and four classical engineering design problems. The experimental and statistical results of 23 test functions showed that the proposed ISCSA could escape from the local optima with higher accuracy and faster convergence than both the CSA and other meta-heuristic optimization algorithms. The calculation results of the four engineering optimization problems showed that compared with other algorithms, ISCSA can solve the practical optimization problem well and has been proved to have strong competitiveness and good performance.
Highlights
Over the past few decades, the complexity of numerical and engineering optimization problems has grown rapidly, prompting researchers to continuously propose and improve different optimization methods to solve the increasingly intractable optimization problems currently encountered [1], [2]
This paper proposed an improved crow search algorithm based on the spiral search mechanism
The idea of Gaussian variation and random perturbation was introduced, which increased the probability that the algorithm jumped out of a local optimum
Summary
Over the past few decades, the complexity of numerical and engineering optimization problems has grown rapidly, prompting researchers to continuously propose and improve different optimization methods to solve the increasingly intractable optimization problems currently encountered [1], [2]. Optimization algorithms can be composed of two types: deterministic techniques and meta-heuristics [3]. Research shows that traditional optimization algorithms will encounter various. It is easy to fall into the local optimum, and the accuracy of the result is highly dependent on the selection of the initial point of the algorithm [4]. The meta-heuristic optimization algorithm is simple, flexible and avoids local optimization. The meta-heuristic optimization algorithm has become an interesting topic for scientists
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