Title: IFOA: an improved forest algorithm for continuous nonlinear optimisation

Authors: Borong Ma; Zhixin Ma; Dagan Nie; Xianbo Li

Addresses: School of Information Science and Engineering, Lanzhou University, Lanzhou, China ' School of Information Science and Engineering, Lanzhou University, Lanzhou, China ' School of Information Science and Engineering, Lanzhou University, Lanzhou, China ' School of Information Science and Engineering, Lanzhou University, Lanzhou, China

Abstract: Forest optimisation algorithm (FOA) is a new evolutionary optimisation algorithm which is inspired by seed dispersal procedure in the forests, suitable for continuous nonlinear optimisation problems. In this paper, an improved forest optimisation algorithm (IFOA) is introduced to improve convergence speed and the accuracy of FOA, and four improvement strategies which include the greedy strategy, waveform step, preferential treatment of best tree and new-type global seeding are proposed to solve continuous nonlinear optimisation problems better. The capability of IFOA has been investigated through the performance of several experiments on well-known test problems and the results prove that IFOA is able to perform global optimisation effectively with high accuracy and convergence speed.

Keywords: forest optimisation algorithm; FOA; evolutionary algorithm; continuous nonlinear optimisation; scientific decision making.

DOI: 10.1504/IJCSE.2019.101344

International Journal of Computational Science and Engineering, 2019 Vol.19 No.3, pp.343 - 353

Received: 22 Aug 2016
Accepted: 06 Feb 2017
Published online: 05 Aug 2019 *

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