Adaptive crow search algorithm and its application in solving constrained optimization problems

Crow Search Algorithm (CSA) is considered as a meta-heuristic optimization algorithm based on intelligent behavior of crows. Although CSA has a significant performance in solving optimization problems, balancing between the exploration and exploitation is affected by awareness probability. However, the value of awareness probability is constant which may cause to trap the algorithm in the local optimum. The present study aimed to balance between the exploration and exploitation in CSA, where the value for the awareness probability is adaptively set up during the algorithm execution. The proposed algorithm is called adaptive crow search algorithm (ACSA). In order to evaluate the algorithm, its performance was compared with some meta-heuristic algorithms and the results showed the significant power of proposed algorithm. In addition, ACSA was applied to solve three constrained optimization problems. Based on the results, significant performance of ACSA was observed compared to the comparing algorithms.

Keywords:

Meta-heuristic Algorithm , Crow Search Algorithm , Constrained Optimization Algorithm.