An improved parallel processing-based strawberry optimization algorithm for drone placement

Abstract

It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. Strawberry optimization (SBA) has been demonstrated to be more effective and superior to current methods in evaluating engineering functions in various engineering problems. Because the SBA is a new method, it has never been used to solve problems involving optimal drone placement. SBA is preferred for optimizing drone placement in this study due to its promising results for nonlinear, mixed, and multimodal problems. Based on the references listed below, no study has investigated the need to develop a parallelized strategy version. Several studies have been conducted on the use of drones for coverage. However, no optimization algorithms have been evaluated regarding time complexity or execution time. Despite what has been said thus far, no study has looked into the significance of a systematic framework for assessing drone coverage techniques using test suits. An optimized drone placement algorithm based on strawberry optimization is presented in the paper. The strawberry optimization algorithm will solve the drone placement problem through parallelization. In addition, the authors deploy test suits that vary in size from small to large. The dataset consists of four categories with three problems each. Results indicate that strawberry optimizers outperform Genetic algorithms (GA) and particle swarm optimization algorithms (PSO) in the number of drones, convergence, and computation time. Furthermore, the proposed approach achieves the best solution in a finite number of steps. In small-scale problems, the performance of all algorithms is convergent. As the size of the data set increases, the superiority of Strawberry optimization algorithms becomes evident. Overall, Strawberry comes out on top for eleven out of twelve comparisons.