Int. J. Bio-Inspired Computation, Vol. 2, No. 5, 2010 291 Copyright © 2010 Inderscience Enterprises Ltd. Artificial physics optimisation: a brief survey Liping Xie*, Ying Tan, Jianchao Zeng and Zhihua Cui Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, No. 66 Waliu Road, Wanbailin District, Taiyuan, Shanxi, 030024, China E-mail: xieliping1978@gmail.com E-mail: tanying@yaoo.com E-mail: zengjianchao@263.net E-mail: cuizhihua@gmail.com *Corresponding author Abstract: 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. Keywords: swarm intelligent; physicomimetics; artificial physics optimisation; APO; global optimisation; virtual force. Reference to this paper should be made as follows: Xie, L., Tan, Y., Zeng, J. and Cui, Z. (2010) ‘Artificial physics optimisation: a brief survey’, Int. J. Bio-Inspired Computation, Vol. 2, No. 5, pp.291–302. Biographical notes: Lingping Xie received her MSc in Computer Application from Taiyuan University of Science and Technology in 2005. She is currently a Lecturer at the Department of Computer Science and Technology, TUST, Taiyuan, PR China. She is now pursuing her PhD in Control Theory and Control Engineering at Lanzhou University of Technology. Her current research interest includes swarm intelligence and swarm robotics. Ying Tan received her BS in Control Theory and Application from Qinghua University in 1994. She is now with the School of Computer and Science and Technology, Taiyuan University of Science and Technology in Shanxi, PR China. Her current research interests include intelligent optimisation. Jianchao Zeng is a Professor and a Tutor of PhD students at the Department of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, PR China. He is now the Vice President of TUST. He received his MSc and PhD in System Engineering from Xi’an Jiaotong University in 1985 and 1990, respectively. His current research focuses on modelling and control of complex systems, intelligent computation, swarm intelligence and swarm robotics. He has published more than 200 international journal and conference papers. Since the 1990s, he has been an invited reviewer of several famous scientific journals. Zhihua Cui is an Associated Professor and the Director of Complex System and Computational Intelligence Laboratory in Taiyuan University of Science and Technology, PR China. In recent years, he has been researching in swarm optimisation and stochastic optimisation. 1 Introduction The focus of our research is to design and build a population-based adaptive, robust and stochastic algorithm based on artificial physics (AP). Our objective is to provide a new approach to the design and analysis of swarm intelligence algorithm. Swarm intelligence algorithms are inspired by the collective behaviours of social insect colonies or animal