Crow Search Algorithm: Theory, Recent Advances, and Applications

Abstract

In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

Hand gesture classification using a novel CNN-crow search algorithm

“…The study in [15] has performed a comparative analysis of CSA with various other meta-heuristic algorithms namely Grey Wolf Optimization, Particle Swarm Optimization, Sine Cosine Algorithm, Bat Algorithm, etc. The Friedman test was conducted in [15] and the results of the evaluation have justified the significance of CSA over the other meta-heuristic algorithms. But there does exist some scalability issues with CSA in cases of handling multi-modal data yielding in low convergence rate.…”

An Improved Crow Search Algorithm Applied to the Phase Swapping Problem in Asymmetric Distribution Systems

“…CSA is a metaheuristic optimization technique inspired by the intelligent performance of crows [29]. Crows are considered to be the smartest birds in nature; they possess a brain much larger in relation to the size of their bodies [36]. In groups, crows show notable traits of intelligence.…”

Enhanced Differential Crossover and Quantum Particle Swarm Optimization for IoT Applications

“…Metaheuristic algorithms are extensively utilized in solving the real life optimization problems. They are iterative and based on social behaviors or natural phenomena [2][3]. The fundamental idea behind natural evolutionary and swarm intelligence algorithms is to use mathematical models for simulating biological and physical structures in nature.…”