An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

Bai Li,Li-Gang Gong,Wen-Lun Yang

doi:10.1155/2014/232704

Bai Li, Li-Gang Gong + Show 1 more

Open Access

https://doi.org/10.1155/2014/232704

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Abstract

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Highlights

Developments in automated and unmanned flight technologies have become an irresistible trend in many countries
Path planning is a critical aspect of the autonomous control module in unmanned combat aerial vehicles (UCAVs), which aims to provide an optimal path from the starting point to the desired destination with the artificial threats and some natural constraints considered
BE-artificial bee colony (ABC) algorithm is applied for the UCAV path planning optimization problem

Summary

Introduction

Developments in automated and unmanned flight technologies have become an irresistible trend in many countries. To avoid enumerating for the global optimums, evolutionary algorithms (EAs) have been well investigated and developed as a primary branch of the heuristic algorithms, such as genetic algorithm (GA) [6], differential evolution algorithm (DE) [7], ant colony optimization algorithm (ACO) [8], particle swarm optimization algorithm (PSO) [9], and artificial bee colony algorithm (ABC) [10] These intelligent algorithms do not necessarily guarantee global convergence, some satisfying results can be acquired after all. A novel ABC algorithm modified by a balance-evolution strategy is applied for this path planning scheme In this new algorithm (which is named BE-ABC), convergence status in the iteration will be fully utilized so as to manipulate the exploration/exploitation accuracy and to make a tradeoff between local exploitation and global exploration.

Combat Field Modeling for UCAV Path Planning

Principles of ABC Relevant Algorithms

Experimental Results and Discussions

Conclusion