A survey on applications of black hole algorithm

Ibrahim, Zuwairie; Khairi, Mohammed Suad; Subari, Norazian; Adam, Asrul; Zulkifli, Md. Yusof; Azlina, Ab. Aziz Nor; Aziz, Nor Hidayati Abdul; Rahman, Tasiransurini Ab; Ibrahim, Suhaimi; Mokhtar, Norrima

doi:10.5954/icarob.2018.gs10-2

Cited by 1 publication

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“…The mathematical model was implemented in C++ language, and it was used linked with the lOF-MH (lEV Optimization Framework -Metaheuristics), version 1.0, developed by the Institute for Advanced Studies (IEAv). Among all the metaheuristics algorithms available in the LOF-MH, the Black Hole-based Optimization (BHO) was selected because it is a recently-developed tool (2013) [14][15] , waiting to be explored, and its small number of control parameters makes it user-friendly.…”

Section: Resultsmentioning

confidence: 99%

Resources Package Modelling Supporting Border Surveillance Operations

2021

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The purpose of this work is to propose a military planning tool capable of providing logistical bases and patrol packages to most effectively support border surveillance. Presently, military patrols are employed along geographical borders to combat transnational crimes; acts such as drug trafficking, smuggling of goods and illegal natural resources exploitation. The patrols make temporary stops within specific time windows at specific places characterised by a high incidence of crime (hotspots). These hotspots have different criticalities within given time windows. To optimise the results, the proposed model allows additional stops in more critical hotspots. It achieves this using a mathematical optimisation model. Considering that there are not adequate logistical-military capacities (logistical bases and patrols) at all needed locations, developing a border surveillance plan that optimises resource use is imperative. The model was run using black hole-based optimisation and a real patrol mission’s database to ensure timely solutions. The solutions were then evaluated in terms of quality (number of bases and patrols, coverage efforts, and travel time) and computational processing time. Next, they were compared with solutions using the traditional method, thereby demonstrating the model’s robustness in providing timely surveillance schemes that ensure high coverage with minimum resources.

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Section: Resultsmentioning

confidence: 99%