Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems

Crow Search Algorithm (CSA) is a promising meta-heuristic method developed based on the intelligent conduct of crows in nature. This algorithm lacks a good representation of its individuals’ memory, and as with many other meta-heuristics it faces a problem in efficiently balancing exploration and exploitation. These defects may lead to early convergence to local optima. To cope with such issues, we proposed a Memory based Hybrid CSA (MHCSA) with the use of Particle Swarm Optimization (PSO) algorithm. This hybridization approach was proposed to reinforce the diversity ability of CSA and balance its search abilities for promising solutions to achieve robust search performance. The memory element of MHCSA was initialized with the best solution ( pbest ) of PSO to exploit the most promising search areas. The best positions of the CSA’s individuals are improved using the best solution found so far ( gbest ) and ( pbest ) of PSO. Another flaw of CSA is the use of fixed flight length and awareness probability for crows to control exploration and exploitation features, respectively. This issue was circumvented here by replacing these constants with adaptive functions in order to provide a better balance between exploration and exploitation over the course of iterations. The competence of MHCSA was revealed by testing it on seventy-three standard and computationally complex benchmark functions. Its applicability was substantiated by solving seven engineering design problems. The results showed that the problem of early convergence was eliminated by MHCSA and that the balance of exploration and exploitation was further improved. Further, MHCSA ranked first among CSA, PSO, robust variants of CSA and other strong competing methods in terms of accuracy and stability.