An Effective Guided Fireworks Algorithm for Solving UCAV Path Planning Problem

被引:1
作者
Alihodzic, Adis [1 ]
Hasic, Damir [1 ]
Selmanovic, Elmedin [1 ]
机构
[1] Univ Sarajevo, Dept Math, Zmaja Bosne 33-35, Sarajevo 71000, Bosnia & Herceg
来源
NUMERICAL METHODS AND APPLICATIONS, NMA 2018 | 2019年 / 11189卷
关键词
Unmanned combat aerial vehicle; Path planning; Swarm intelligence; Metaheuristics; Fireworks algorithm;
D O I
10.1007/978-3-030-10692-8_3
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The use of the unmanned aerial vehicles is rapidly growing in the ever more extensive range of applications where the military is the oldest ones. One of the fundamental problems in the unmanned combat aerial vehicles (UCAV) control is the path planning problem that refers to optimization of the flight route subject to various constraints inside the battlefield environments. Since the number of control points is high as well as the number of radars, the traditional methods could not produce acceptable results when tackling this problem. In this paper, we propose an adjustment of the recent guided fireworks algorithm from the class of swarm intelligence algorithms for locating the optimal path by unmanned combat aerial vehicle taking into consideration fuel consumption and safety degree. For experimental purposes, we compared it with eight different methods from the literature. Based on the experimental results, it can be concluded that our proposed approach is robust, exhibits better performance in almost all cases.
引用
收藏
页码:29 / 38
页数:10
相关论文
共 16 条
  • [1] Alihodzic A, 2017, 2017 25TH TELECOMMUNICATION FORUM (TELFOR), P804
  • [2] Fireworks Algorithm with New Feasibility-Rules in Solving UAV Path Planning
    Alihodzic, Adis
    [J]. 2016 3RD INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE (ISCMI 2016), 2016, : 53 - 57
  • [3] [Anonymous], 2005, P 16 INT FED AUT CON
  • [4] [Anonymous], 2018, Unmanned aerial vehicle (UAV) market by application, class, system (UAV platforms, UAV payloads, UAV GCS, UAV data links, UAV launch and recovery systems), UAV type, mode of operation, range, point of sale, MTOW, and region-global forecast to 2025
  • [5] [Anonymous], 2012, Int. J. Hybrid Inf. Technol.
  • [6] Brajevic I., 2018, J INTELL MANUF, P1
  • [7] Brintaki A.N., 2005, INT J-TORONTO, V5, P487, DOI DOI 10.1007/BF02941133
  • [8] Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm
    Dolicanin, Edin
    Fetahovic, Irfan
    Tuba, Eva
    Capor-Hrosik, Romana
    Tuba, Milan
    [J]. STUDIES IN INFORMATICS AND CONTROL, 2018, 27 (01): : 15 - 24
  • [9] Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments
    Duan, Hai-bin
    Zhang, Xiang-yin
    Wu, Jiang
    Ma, Guan-jun
    [J]. JOURNAL OF BIONIC ENGINEERING, 2009, 6 (02) : 161 - 173
  • [10] Khatib W, 1998, LECT NOTES COMPUT SC, V1498, P683, DOI 10.1007/BFb0056910