Authors: Xiaodong Guo; Xueliang Zhang; Lifang Wang
Addresses: School of Electronic and Information Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China ' School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China ' School of Computer Science and Engineering, Taiyuan University of Science and Technology, Taiyuan, Shanxi, 030024, China
Abstract: Inspired by probability estimation for code-words in adaptive arithmetic coding, a fruit fly optimisation algorithm with adaptive sign processing is presented to solve a class of optimisation problems with negative optimum point. Sign probability of previously optimal solutions are dynamically calculated, and signs of smell concentration judgement value are adaptively created, then drosophila swarms in this way will search more fast and stationary than the ones gained in random manner. Simulation results for test functions show the adaptive manner in sign processing is efficient and potential for fruit fly optimisation algorithm.
Keywords: fruit fly optimisation; function optimisation; adaptive sign processing; random sign processing; negative optimum point; sign probability; drosophila swarms; simulation.
International Journal of Computing Science and Mathematics, 2015 Vol.6 No.6, pp.538 - 545
Received: 08 May 2015
Accepted: 20 Jun 2015
Published online: 13 Dec 2015 *