The selection strategy of mass functions in artificial physics optimisation algorithm Online publication date: Sat, 16-Aug-2014
by Liping Xie; Ying Tan; Jianchao Zeng; Zhihua Cui
International Journal of Modelling, Identification and Control (IJMIC), Vol. 18, No. 3, 2013
Abstract: Inspired by physicomimetics, artificial physics optimisation (APO) is a novel population-based stochastic algorithm. In APO framework, the mass of each individual corresponds to a user-defined function of the value of an objective to be optimised, which can supply some important information for searching global optima. There are many functions that can be used as mass function, and no doubt some will be better than others for specific optimisation problems or perhaps classes of problems. This paper proposes the basic requirement and design method of mass function, and classifies mass functions into four different types of curvilinear functions according to their curvilinear styles, such as linear function, convex function, and concave function, etc. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations.
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