Mass functions design of artificial physics optimisation algorithm for constrained optimisation problem
Liping Xie, Jian Yin, Hongli Zhang, Ying Tan · International Journal of Computer Applications in Technology · 2013
Artificial physics optimisation (APO) is a novel population-based stochastic algorithm inspired by physicomimetics. APO with the feasibility and dominance method (EAD-APO) is employed to solve constrained optimisation problems. In EAD-APO, the mass of each feasible individual corresponds to a user-defined function of the value of an objective to be optimised, and the mass of each infeasible individual corresponds to a user-defined function of the constraint violation value, which can supply some important information for searching global optima. There are many functions 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 regulation and design method of mass function, and classifies mass functions into three different types of curvilinear functions according to their curvilinear styles, such as linear function, convex function, and concave function. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations.