Forest Optimization Algorithm
作者：
Highlights：
• We proposed Forest Optimization Algorithm (FOA) as a new evolutionary algorithm.
• We compared FOA with GA and PSO on 4 test functions in 2, 5 and 10 dimensions.
• FOA needs fewer evaluations than GA and PSO in almost all of the test functions.
• Testing FOA in a smooth unimodal function shows its better results than GA and PSO.
• FOA improved KNN classifier by feature weighting as a real problem in data mining.
摘要
•We proposed Forest Optimization Algorithm (FOA) as a new evolutionary algorithm.•We compared FOA with GA and PSO on 4 test functions in 2, 5 and 10 dimensions.•FOA needs fewer evaluations than GA and PSO in almost all of the test functions.•Testing FOA in a smooth unimodal function shows its better results than GA and PSO.•FOA improved KNN classifier by feature weighting as a real problem in data mining.
论文关键词：Forest Optimization Algorithm (FOA),Evolutionary algorithms,Nonlinear optimization,Data mining,Feature weighting
论文评审过程：Available online 17 May 2014.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.05.009
