Abstract
Artificial physics optimisation (APO) algorithm is an optimisation algorithm based on physicomimetics framework. Driven by virtual force, a population of sample individuals searches a global optimum in the problem space. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimised. It is an important parameter to influence the performance of APO algorithm. Therefore, in this paper, the authors make a study on the selection principle of mass on numerical optimisation problems. According to the curvilinear style of the mass functions, they are classified into three different types of curvilinear functions: convex function, linear function and concave function. To make a deep insight, several versions of APO algorithm with different mass functions are used to solve two type benchmarks: unimodal and multimodal functions. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations. In addition, the performance of APO algorithm is compared with that of the modified electromagnetism-like (EM), differential evolution (DE), evolutionary algorithm (EA) and particle swarm optimisation (PSO) for multidimensional numeric benchmarks. The simulation results show that APO algorithm is competitive.
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