Artificial physics optimisation: a brief survey
As a new swarm intelligence algorithm, artificial physics optimisation (APO) is based on physicomimetics to solve global optimisation problems. A relationship of mapping between AP approach and population-based optimisation algorithm is constructed by comparing the similarities and differences of physical individual and ideal particle. Each particle is treated as physical individual with mass, position and velocity. Force law and mass function are preliminary analysed through providing several selection strategies. The convergent condition of APO is derived by theoretically analysing. The vector model of APO is constructed, an extended APO including each individual's best history position and a local APO with some simple topologies are presented inspired by the useful experiences and limited sense and interaction among individuals in swarm foraging processes. The implementations of APO and its improvements are applied to multidimensional numeric benchmark functions and the simulation results confirm APO is effective.
artificial physics optimisation swarm intelligent apo physicomimetics global optimisation virtual force