Dynamic crow search algorithm based on adaptive parameters for large-scale global optimization

Despite the good performance of Crow Search Algorithm (CSA) in dealing with global optimization problems, unfortunately it is not case respect to convergence performance. Conventional CSA exploration and exploitation are strongly dependent on proper setting awareness probability (AP) flight length (FL) parameters. In each problem, AP FL parameters set an ad hoc manner their values do change over process. To this date, there no analytical approach adjust best values. This presents a major drawback apply complex practical problems. Hence, conventional used only for limited problems due fact that fixed frequently trapped into local optimum. present paper, enhanced version called dynamic crow search algorithm (DCSA) proposed overcome drawbacks CSA. DCSA, two modifications basic made. The first modification concerns continuous adjustment leading where will be adjusting linearly process according generalized Pareto density function. provide more capability as well pre-final solutions. second improvement CSA’s swarm diversity lead high accuracy, fast rate. effectiveness validated using experimental series 13 benchmark functions. Experimental results highly proved modified compared terms rate, final addition, comparison recent similar algorithms revealed DCSA gives superior efficiency.