An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning

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.

A Novel Artificial Bee Colony Algorithm with an Overall-Degradation Strategy and Its Performance on the Benchmark Functions of CEC 2014 Special Session

“…As a feasible suggestion, combining such overall-degradation strategy with other existing strategies may work (e.g., see Refs. [1,7,31]).…”

“…In addition, the framework of ABC is relatively simple and clear, making it easy to acquire satisfactory results at a low computational cost. Such merits have given rise to applications of ABC spanning across various areas, such as trajectory planning [5][6][7], structure optimization [8][9][10], clustering [11], machine learning [12], scheduling [13][14][15][16], image recognition [17][18][19][20] etc. Benchmark Functions of CEC 2014 Special Session Regarding the modifications ever made for the conventional ABC, from the author's viewpoint, the prevailing ways can be broadly classified into three categories.…”

Quantum Wind Driven Optimization for Unmanned Combat Air Vehicle Path Planning

“…The forces mentioned above can be added to the Equation (6). The equation can be described as Equation (11):…”

“…Wang et al proposed a new modified firefly algorithm (MFA) based on a modification in exchange information to solve the UCAV path planning problem [5]. Li et al proposed a novel artificial bee colony algorithm (ABC) improved by a balance-evolution strategy to solve the problem [6]. Zhou et al proposed a wolf colony search algorithm (WCA) based on the complex method to solve the UCAV path planning problem [7].…”

Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots

“…Trajectory planning has become a critical aspect of automation science; the applications of this process range from satellite orbit transfer [2], missile guidance [3], unmanned aerial vehicle navigation [4][5][6], anti-submarine search [7,8], hazardous environment exploration [9] and autonomous parking assistance [10,11]. In this work, we focus on the trajectory planning of car-like robots.…”