TY - CHAP
T1 - Moths–Flame Optimization Algorithm
AU - Okwu, Modestus O.
AU - Tartibu, Lagouge K.
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Moth–flame optimization (MFO) algorithm is a simple and easy to implement, nature-inspired, meta-heuristic algorithm. This chapter covers the introduction to moth flame optimization algorithm, steps for generating moths randomly within the neighbourhood or solution space and solved numerical example are provided. The fitness value for each moth is calculated and the best position obtained is tagged by flame. The updating process takes place after which the process is repeated until a point where criteria for termination is attained. The MFO model was implemented in MATLAB, to illustrate the approach, 30 search agents were considered (n = 30) and the maximum number of iterations were set to 100. The best solution obtained by MFO [1.7999 0.19996] and the best optimal value of the objective function found by MFO is 84. MFO algorithm can be used in diverse areas of operation for scheduling, estimation, simulation, and control.
AB - Moth–flame optimization (MFO) algorithm is a simple and easy to implement, nature-inspired, meta-heuristic algorithm. This chapter covers the introduction to moth flame optimization algorithm, steps for generating moths randomly within the neighbourhood or solution space and solved numerical example are provided. The fitness value for each moth is calculated and the best position obtained is tagged by flame. The updating process takes place after which the process is repeated until a point where criteria for termination is attained. The MFO model was implemented in MATLAB, to illustrate the approach, 30 search agents were considered (n = 30) and the maximum number of iterations were set to 100. The best solution obtained by MFO [1.7999 0.19996] and the best optimal value of the objective function found by MFO is 84. MFO algorithm can be used in diverse areas of operation for scheduling, estimation, simulation, and control.
UR - http://www.scopus.com/inward/record.url?scp=85096227723&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61111-8_12
DO - 10.1007/978-3-030-61111-8_12
M3 - Chapter
AN - SCOPUS:85096227723
T3 - Studies in Computational Intelligence
SP - 115
EP - 123
BT - Studies in Computational Intelligence
PB - Springer Science and Business Media Deutschland GmbH
ER -