Abstract
Crow search algorithm (CSA) simulate the intelligent behavior of crows to solve multi-dimensional, linear and nonlinear problems with appreciable. Despite high performance of CSA, stagnation in local optima and slow convergence speed are two probable problems in solving challenging optimization problems. In this paper, the standard CSA is improved to enhance its exploration and exploitation capacities and convergence speed by introducing adaptive inertia weight factor and roulette wheel selection scheme. Performance of the improved CSA (ICSA) is assessed by implementing it on a range of standard unconstrained benchmark functions having different characteristics. The results of optimization obtained using the ICSA algorithm are validated by comparing them with those obtained using the basic CSA and other optimization algorithms available in the literature.
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