Path planning of multiple UAVs using MMACO and DE algorithm in dynamic environment

Abstract

Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue. This research addresses the path planning of multi-colonies with multiple unmanned aerial vehicles in dynamic environment. To observe the model of whole scenario, we combine maximum–minimum ant colony optimization and differential evolution to make metaheuristic optimization algorithm. Our designed algorithm, controls the deficiencies of present classical ant colony optimization and maximum–minimum ant colony optimization, has the contradiction among the excessive information and global optimization. Moreover, in our proposed algorithm, maximum–minimum ant colony optimization is used to lemmatize the pheromone and only best ant of each colony is able to construct the path. However, the path escape by maximum–minimum ant colony optimization and it treated as the object for differential evolution constraints. Now, it is ensuring to find the best global colony, which provides optimal solution for the entire colony. Furthermore, the proposed approach has an ability to increase the robustness while preserving the global convergence speed. Finally, the simulation experiment results are performed under the rough dynamic environment containing some high peaks and mountains.

Critical analysis: bat algorithm-based investigation and application on several domains

“…Wang et al (2012), Ali et al (2020a) have proposed a new bat algorithm with a mutation to solve the route planning problems of unmanned warship aerial vehicles (Ali et al , 2020b). The standard bat algorithm has been used for solving the UCAV path planning problem before they applied a modification to the original BA to switch between bats in generating new solutions step.…”

A new K-means grey wolf algorithm for engineering problems

“…Balancing between exploration and exploitation is one of the issues that are related to GWO (Luo et al, 2019;Mittal et al, 2016). Despite having the efficient capability, GWO has poor performance in the search space because sometimes it sticks in local optima (Jadhav and Gomathi, 2018;Ali et al, 2020aAli et al, , 2020b). Slow convergence rates and low accuracy are problems that can be enhanced by researchers (Wu et al, 2020;Wang and Li, 2019).…”

“…Slow convergence rates and low accuracy are problems that can be enhanced by researchers (Wu et al, 2020;Wang and Li, 2019). Therefore, different methods are used by researchers to improve the capacity of optimization algorithms and solve optimization problems (Ali et al, 2020a(Ali et al, , 2020bChakraborty, 2017;Rashid et al, 2019;Kohli and Arora, 2018). For example, a modified GWO was proposed to tune the parameters of the recurrent neural network (Panwar et al, 2018).…”

Multi-step network traffic prediction using echo state network with a selective error compensation strategy

“…For the first problem, there are many optimization algorithms that can be used to realize optimal selection of the four model parameters. These are, to name a few only: genetic algorithm (GA) by Mayer and Gróf (2020); particle swarm optimization (PSO) by Ali and Han (2021); simulated annealing (SA) by Khurshid et al (2021); ant colony optimization (ACO) by Ali et al (2020, 2021); harmony search (HS) by Mousavi et al (2021); brain storm optimization (BSO) algorithm by Li et al (2020) and Han et al (2021); black hole (BH) by Li et al (2015); bat algorithm (BA) by Wang et al (2020b); pigeon inspired optimization algorithm (PIO) by Ge et al (2020) and so on. A new fruit–fly optimization algorithm (FOA) was proposed by Pan in 2012, which has had advantages of simple principle, easy understanding and programming, less control parameters and alike.…”