Simulated Raindrop algorithm for global optimization

In this paper, we propose a novel single-solution based metaheuristic algorithm called Simulated Raindrop (SRD). The SRD algorithm is inspired by the principles of raindrops. When rain falls on the land, it normally flows from higher altitude to a lower due to gravity, while choosing the optimum path towards the lowest point on the landscape. We compared the performance of simulated annealing (SA) against the proposed SRD method on 8 commonly utilized benchmark functions. Experimental results confirm that SRD outperforms SA on all test problems in terms of variant performance measures, such as convergence speed, accuracy of the solution, and robustness.