Abstract
In this paper, a method based on a Multiobjective Multi-Verse Optimizer (MOMVO) is proposed and successfully implemented to solve the unmanned aerial vehicles’ path planning problem. The generation of each coordinate of the aircraft is reformulated as a multiobjective optimization problem under operational constraints. The shortest and smoothest path by avoiding all obstacles and threats is the solution of such a hard optimization problem. A set of competitive metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Grey Wolf Optimizer (MOGWO), Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are retained as comparison tools for the problem’s resolution. To assess the performance of the reported algorithms and conclude about their effectiveness, an empirical study is firstly performed for solving different multiobjective test functions from the literature. These algorithms are then used to obtain a set of optimal Pareto solutions for the multi-criteria path planning problem. An efficient Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) of Multi Criteria Decision-Making (MCDM) model is investigated to find the optimal solution from the non-dominant ones. Demonstrative results and statistical analysis are presented and compared in order to show the effectiveness of the proposed MOMVO-based path planning technique.
Highlights
The Unmanned Aerial Vehicles (UAVs) have shown their commitment in various military and civil applications [1, 2]
In order to evaluate the performance of the reported competitive algorithms Multiobjective Salp Swarm Algorithm (MSSA), Multiobjective Multi-Verse Optimizer (MOMVO), MOGWO, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multiobjective Particle Swarm Optimization (MOPSO) for problem (5), an empirical study is firstly conducted based on a benchmark of 9 standard multiobjective test problems from the CEC’2009 test suite [32]
The path planning problem for unmanned aerial vehicles is reformulated by transforming the generation of each flight waypoint into a constrained multiobjective optimization problem
Summary
The Unmanned Aerial Vehicles (UAVs) have shown their commitment in various military and civil applications [1, 2]. In [22], an improved multiobjective ACO algorithm has been adopted in which the objective function for optimization is formulated to make UAV drone following a short, safe and smooth path Such an algorithm assumes that the environment is known in advance. Based on the aforementioned studies, and regarding the drawbacks of the cited methods especially in terms of complexity and time consuming, the main contribution of this paper is the development of a novel strategy of reformulation and solving of a multi-criteria path planning problem under operational constraints based on a recent and unified MOMVO algorithm. The proposed MOMVO-based method allows the UAVs to autonomously calculate the optimum or near optimal path from the starting point to the target while avoiding all threats and obstacles considered in the flight environment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
