Application of an improved discrete crow search algorithm with local search and elitism on a humanitarian relief case

Abstract

This paper demonstrates an application of the improved crow search algorithm (I-CSA), which is a modified version of the crow search algorithm (CSA). CSA is a recent, nature-inspired meta-heuristic optimization algorithm. I-CSA differs from CSA by allowing application on a discrete problem, which is P-median and fortifying faster convergence to an optimal or near-optimal solution. Improvements are provided by local searches which support escaping from local optima or convergence to the optimal solution, elitism enhances the intensification through the utilization of nodes by selecting the most frequent centers that appeared in hiding better locations for local search, on the P-median problem. The application of the I-CSA is structured in three phases. In the first phase, parameters of I-CSA are analyzed and optimized using well-known data tests. The test datasets for the application of I-CSA on the P-median problem were retrieved from the OR-library to present the effectiveness and applicability of I-CSA. In the second phase, 40-pmed test problems from the library are solved using I-CSA and the results are compared with known optimal results and recorded results of other meta-heuristic approaches. In addition, Wilcoxon signed-rank test is applied in order to demonstrate the performance of I-CSA compared to well-known algorithms. The results of the proposed method demonstrated a faster convergence rate and better solution in most cases when compared with the standard CSA and other well-known meta-heuristic approaches. Finally, the proposed I-CSA approach is tested on a real-life case problem including 2121 nodes in Tunceli, Turkey. Obtaining the optimal results in a reasonable time indicates that the potential of the I-CSA is high and promising. In nutshell, this paper presents an improvement to CSA and evaluates its performance in a three-phase test procedure.