TY - GEN
T1 - Finding solutions of the set covering problem with an Artificial Fish Swarm Algorithm Optimization
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Olguín, Eduardo
AU - Misra, Sanjay
AU - Villablanca, Sebastián Mansilla
AU - Rubio, Álvaro Gómez
AU - Jaramillo, Adrián
AU - Salas, Juan
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - The Set Covering Problem (SCP) is a matrix that is composed of zeros and ones and consists in finding a subset of zeros and ones also, in order to obtain the maximum coverage of necessities with a minimal possible cost. In this world, it is possible to find many practical applications of this problem such as installation of emergency services, communications, bus stops, railways, airline crew scheduling, logical analysis of data or rolling production lines. SCP has been solved before with different nature inspired algorithms like fruit fly optimization algorithm. Therefore, as many other nature inspired metaheuristics which imitate the behavior of population of animals or insects, Artificial Fish Swarm Algorithm (AFSA) is not the exception. Although, it has been tested on knapsack problem before, the objective of this paper is to show the performance and test the binary version of AFSA applied to SCP, with its main steps in order to obtain good solutions. As AFSA imitates a behavior of a population, the main purpose of this algorithm is to make a simulation of the behavior of fish shoal inside water and it uses the population as points in space to represent the position of fish in the shoal.
AB - The Set Covering Problem (SCP) is a matrix that is composed of zeros and ones and consists in finding a subset of zeros and ones also, in order to obtain the maximum coverage of necessities with a minimal possible cost. In this world, it is possible to find many practical applications of this problem such as installation of emergency services, communications, bus stops, railways, airline crew scheduling, logical analysis of data or rolling production lines. SCP has been solved before with different nature inspired algorithms like fruit fly optimization algorithm. Therefore, as many other nature inspired metaheuristics which imitate the behavior of population of animals or insects, Artificial Fish Swarm Algorithm (AFSA) is not the exception. Although, it has been tested on knapsack problem before, the objective of this paper is to show the performance and test the binary version of AFSA applied to SCP, with its main steps in order to obtain good solutions. As AFSA imitates a behavior of a population, the main purpose of this algorithm is to make a simulation of the behavior of fish shoal inside water and it uses the population as points in space to represent the position of fish in the shoal.
KW - Artificial Fish Swarm Optimization Algorithm
KW - Combinatorial optimization
KW - Metaheuristics
KW - Set Covering Problem
UR - http://www.scopus.com/inward/record.url?scp=84978792180&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42085-1_13
DO - 10.1007/978-3-319-42085-1_13
M3 - Conference contribution
AN - SCOPUS:84978792180
SN - 9783319420844
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 166
EP - 181
BT - Computational Science and Its Applications - 16th International Conference, ICCSA 2016, Proceedings
A2 - Apduhan, Bernady O.
A2 - Murgante, Beniamino
A2 - Misra, Sanjay
A2 - Taniar, David
A2 - Torre, Carmelo M.
A2 - Rocha, Ana Maria A.C.
A2 - Wang, Shangguang
A2 - Gervasi, Osvaldo
A2 - Stankova, Elena
PB - Springer Verlag
T2 - 16th International Conference on Computational Science and Its Applications, ICCSA 2016
Y2 - 4 July 2016 through 7 July 2016
ER -