Research Information System
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, vol.28, no.4, 2025 (SCI-Expanded)
Unpredictable capabilities and versatile usage purposes of the unmanned aerial vehicles (UAVs) changed the future projections of the developed countries and researchers focused on solving challenging problems about these vehicles. Path planning is among the challenging problems about the UAV systems and requires a solution routine that optimizes some objectives defined for the enemy threats, fuel consumption and turning maneuvers. In this study, a greedy heuristic approach utilizing from the description of the geometric properties of the path planning problem and generating two eligible path candidates was integrated into the Immune Plasma algorithm (IP algorithm or IPA) and then a new variant called the DUALIPA exploiting the paths of the greedy approach was introduced. As its name implies, DUALIPA has two populations being executed simultaneously and communicated with each other. While the first population of the DUALIPA is initialized with the help of the first path found by the greedy method, the second population is initialized with the help of the second path of the same greedy method. Moreover, for exploiting the qualified solutions of the populations, a deterministic plasma collection procedure and specialized treatment schema that removes the necessity of using the control parameters belonging to the standard IPA were designed and combined with the DUALIPA. The path planning capabilities of the DUALIPA were investigated by using twelve test cases of three different battlefield scenarios in detail. The results found by the DUALIPA were compared to the results of other fourteen path planners based on meta-heuristics. Comparative studies showed that the DUALIPA performs better than all fourteen techniques for eleven of twelve test cases by completing a run of it nearly two times faster.