Search-evasion path planning for submarines using the Artificial Bee Colony algorithm

Abstract

Submarine search-evasion path planning aims to acquire an evading route for a submarine so as to avoid the detection of hostile anti-submarine searchers such as helicopters, aircraft and surface ships. In this paper, we propose a numerical optimization model of search-evasion path planning for invading submarines. We use the Artificial Bee Colony (ABC) algorithm, which has been confirmed to be competitive compared to many other nature-inspired algorithms, to solve this numerical optimization problem. In this work, several search-evasion cases in the two-dimensional plane have been carefully studied, in which the anti-submarine vehicles are equipped with sensors with circular footprints that allow them to detect invading submarines within certain radii. An invading submarine is assumed to be able to acquire the real-time locations of all the anti-submarine searchers in the combat field. Our simulation results show the efficacy of our proposed dynamic route optimization model for the submarine search-evasion path planning mission.

Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms

“… Improve heuristic and intelligent algorithms such as Artificial Bee Colony algorithm [125,126], Cuckoo Search [127,128], Shuffled Frog Leaping [129,130] reported to have achieved good results in 3D workspace should be researched on how to develop it hybrid with Q-learning to solve WMR path planning problem in complex 2D workspace.…”

A two-layer optimization framework for UAV path planning with interval uncertainties

“…The details of the CIAM and ABC algorithm are not included in this paper due to space constraints. Interested readers should refer to [17] and [21] for details. We will tackle the second issue in the next section.…”

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

“…The conventional ABC algorithm employs three kinds of bees: scout bees searching for nectar sources randomly, employed bees associated with specific nectar sources, and onlooker bees following the guidance of employed bees. Typically, half of a bee colony would consist of the employed bees and the other half the onlooker bees [28].…”