Forest Optimization Algorithm

•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.

In this article, a new evolutionary algorithm, Forest Optimization Algorithm (FOA), suitable for continuous nonlinear optimization problems has been proposed. It is inspired by few trees in the forests which can survive for several decades, while other trees could live for a limited period. In FOA, seeding procedure of the trees is simulated so that, some seeds fall just under the trees, while others are distributed in wide areas by natural procedures and the animals that feed on the seeds or fruits. Application of the proposed algorithm on some benchmark functions demonstrated its good capability in comparison with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Also we tested the performance of FOA on feature weighting as a real optimization problem and the results of the experiments showed the good performance of FOA in some data sets from the UCI repository.