Different force laws driving artificial physics optimisation algorithm for constrained optimisation problem Online publication date: Thu, 19-Nov-2015
by Liping Xie; Jianchao Zeng; Jian Yin
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 9, No. 3, 2015
Abstract: Inspired by physical force, Artificial Physics Optimisation (APO) algorithm is a novel stochastic based on a physicomimetics framework. Driven by virtual force, a population of sample individuals searches for a global optimum in the problem space. The force law is a key problem associated with the performance of APO algorithm significantly. In the paper, an APO algorithm with the Feasibility and Dominance (FAD) method (FAD-APO) is employed to solve constrained optimisation problems. Three different force laws are constructed between the feasible individuals and infeasible individuals, which drive all individuals to search for a global optimum in the constrained problem space. Simulation results show that FAD3-APO algorithm may generally perform better than FAD1-APO and FAD2-APO; it is the most stable and effective among the three versions of FAD-APO algorithms. Meanwhile, a comparison with other population-based heuristics shows that the FAD-APO algorithm is competitive on some test function.
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