Different force laws driving artificial physics optimisation algorithm for constrained optimisation problem

作者：Xie Liping; Zeng Jianchao^*; Yin Jian

来源：International Journal of Wireless and Mobile Computing, 2015, 9(3): 290-299.

DOI：10.1504/IJWMC.2015.073099

• Artificial physics
• Constrained optimisation problems
• Constrained problem
• Feasibility-based rule
• Force law
• Optimisations
• Physicomimetics
• Test functions

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 populationbased heuristics shows that the FAD-APO algorithm is competitive on some test function.

• 出版日期2015