Abstract
This chapter provides an introduction to the crow search algorithm (CSA) as well as a discussion to keep scholars engaged in swarm intelligence techniques and optimization problem-solving. CSA is a newly created swarm intelligence program that mimics crow behavior in the storage and retrieval of surplus food. There is a solution that can be found by storing food locations randomly in the surrounding environment, which is referred to as the “search space” by optimization theory. A global optimum solution is one where the most food is kept, and the objective function is how much food there is. CSA uses crows’ cognitive behavior as a model to solve numerous optimization issues. It has received a lot of attention because of its benefits, such as ease of use, a small number of parameters, and adaptability. Hybrid, modified, and multiobjective CSA variants are included in this survey. A detailed assessment of CSA applications in certain fields including power, computer science, machine learning, and civil engineering was also conducted based on the analysis of literature published by publishers like IEEE, Elsevier, and Springer. It has also been shown that CSA has both benefits and downsides by performing various comparison trials with other published peers.
Published Version
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