Abstract
Crow search algorithm (CSA) is a nature-inspired metaheuristic algorithm that is emboldened by the social activity of intelligent bird. Crow preserves their surplus food from other crows which gives information regarding local search and limits to the solution in search space. The behavior of crow to search the food is formulated as CSA for solving optimization problems. However, it suffers from poor exploitation to the local search. Inspired from artificial bee colony (ABC), the search equation of CSA is modified in order to improve the exploitation guided by global best solution obtained so far in the search equation. The superiority of the global best guided CSA (G-CSA) is realized by a comparative analysis with DE, PSO, SCA, CSA algorithms on various benchmark equations.
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