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An Evolutionary Algorithm for Optimal Multi-Direction Search Route in Search and Rescue Operation

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@article{IJASEIT8690,
   author = {Ngoc Ha Pham and Minh Duc Nguyen},
   title = {An Evolutionary Algorithm for Optimal Multi-Direction Search Route in Search and Rescue Operation},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {4},
   year = {2019},
   pages = {1199--1204},
   keywords = {search and rescue; optimal search algorithm; BFOA; multi-direction search},
   abstract = {Enhancing the effectiveness of search and rescue operation at sea is the utmost importance. Once the search area has been identified, the success of search and rescue operations depends on search and rescue vessel swept the probability area of the distress object with the minimum search time, this is an important element to the success of search and rescue operation as it minimizes the risk and the cost for search and rescue team. However, determining the optimal search route to find the distress object is extremely complex because weather conditions, winds, waves, and currents always change constantly, whereas the condition of the search and rescue vessel also changes. The present article proposes the use of a bacterial foraging optimization algorithm and applies it to finding the optimal multi-direction search route for search and rescue vessel. The cost function takes into the consideration of the total search time as well as the probability of quick find of the object in distress.  In this paper, the Monte Carlo simulation method is used to predict the most probable drift area of the distress object, and then a swarm of 10 bacteria is deployed for searching the optimal SAR route. It can be seen from the calculation result that the bacteria swarm has concentrated rather well after just several generations. The optimal search path is reasonable for the dominant weather conditions and is accordance with the popular code of practice.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8690},
   doi = {10.18517/ijaseit.9.4.8690}
}

EndNote

%A Pham, Ngoc Ha
%A Nguyen, Minh Duc
%D 2019
%T An Evolutionary Algorithm for Optimal Multi-Direction Search Route in Search and Rescue Operation
%B 2019
%9 search and rescue; optimal search algorithm; BFOA; multi-direction search
%! An Evolutionary Algorithm for Optimal Multi-Direction Search Route in Search and Rescue Operation
%K search and rescue; optimal search algorithm; BFOA; multi-direction search
%X Enhancing the effectiveness of search and rescue operation at sea is the utmost importance. Once the search area has been identified, the success of search and rescue operations depends on search and rescue vessel swept the probability area of the distress object with the minimum search time, this is an important element to the success of search and rescue operation as it minimizes the risk and the cost for search and rescue team. However, determining the optimal search route to find the distress object is extremely complex because weather conditions, winds, waves, and currents always change constantly, whereas the condition of the search and rescue vessel also changes. The present article proposes the use of a bacterial foraging optimization algorithm and applies it to finding the optimal multi-direction search route for search and rescue vessel. The cost function takes into the consideration of the total search time as well as the probability of quick find of the object in distress.  In this paper, the Monte Carlo simulation method is used to predict the most probable drift area of the distress object, and then a swarm of 10 bacteria is deployed for searching the optimal SAR route. It can be seen from the calculation result that the bacteria swarm has concentrated rather well after just several generations. The optimal search path is reasonable for the dominant weather conditions and is accordance with the popular code of practice.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8690
%R doi:10.18517/ijaseit.9.4.8690
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 4
%@ 2088-5334

IEEE

Ngoc Ha Pham and Minh Duc Nguyen,"An Evolutionary Algorithm for Optimal Multi-Direction Search Route in Search and Rescue Operation," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, pp. 1199-1204, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.4.8690.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Pham, Ngoc Ha
AU  - Nguyen, Minh Duc
PY  - 2019
TI  - An Evolutionary Algorithm for Optimal Multi-Direction Search Route in Search and Rescue Operation
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 4
Y2  - 2019
SP  - 1199
EP  - 1204
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - search and rescue; optimal search algorithm; BFOA; multi-direction search
N2  - Enhancing the effectiveness of search and rescue operation at sea is the utmost importance. Once the search area has been identified, the success of search and rescue operations depends on search and rescue vessel swept the probability area of the distress object with the minimum search time, this is an important element to the success of search and rescue operation as it minimizes the risk and the cost for search and rescue team. However, determining the optimal search route to find the distress object is extremely complex because weather conditions, winds, waves, and currents always change constantly, whereas the condition of the search and rescue vessel also changes. The present article proposes the use of a bacterial foraging optimization algorithm and applies it to finding the optimal multi-direction search route for search and rescue vessel. The cost function takes into the consideration of the total search time as well as the probability of quick find of the object in distress.  In this paper, the Monte Carlo simulation method is used to predict the most probable drift area of the distress object, and then a swarm of 10 bacteria is deployed for searching the optimal SAR route. It can be seen from the calculation result that the bacteria swarm has concentrated rather well after just several generations. The optimal search path is reasonable for the dominant weather conditions and is accordance with the popular code of practice.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8690
DO  - 10.18517/ijaseit.9.4.8690

RefWorks

RT Journal Article
ID 8690
A1 Pham, Ngoc Ha
A1 Nguyen, Minh Duc
T1 An Evolutionary Algorithm for Optimal Multi-Direction Search Route in Search and Rescue Operation
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 4
YR 2019
SP 1199
OP 1204
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 search and rescue; optimal search algorithm; BFOA; multi-direction search
AB Enhancing the effectiveness of search and rescue operation at sea is the utmost importance. Once the search area has been identified, the success of search and rescue operations depends on search and rescue vessel swept the probability area of the distress object with the minimum search time, this is an important element to the success of search and rescue operation as it minimizes the risk and the cost for search and rescue team. However, determining the optimal search route to find the distress object is extremely complex because weather conditions, winds, waves, and currents always change constantly, whereas the condition of the search and rescue vessel also changes. The present article proposes the use of a bacterial foraging optimization algorithm and applies it to finding the optimal multi-direction search route for search and rescue vessel. The cost function takes into the consideration of the total search time as well as the probability of quick find of the object in distress.  In this paper, the Monte Carlo simulation method is used to predict the most probable drift area of the distress object, and then a swarm of 10 bacteria is deployed for searching the optimal SAR route. It can be seen from the calculation result that the bacteria swarm has concentrated rather well after just several generations. The optimal search path is reasonable for the dominant weather conditions and is accordance with the popular code of practice.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8690
DO  - 10.18517/ijaseit.9.4.8690