Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Physicomimetics for Mobile Robot Formations
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
A Note on the Extended Rosenbrock Function
Evolutionary Computation
A global optimization based on physicomimetics framework
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
An Extended Artificial Physics Optimization Algorithm for Global Optimization Problems
ICICIC '09 Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control
On mass effects to artificial physics optimisation algorithm for global optimisation problems
International Journal of Innovative Computing and Applications
The vector model of artificial physics optimization algorithm for global optimization problems
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm
International Journal of Bio-Inspired Computation
An overview of physicomimetics
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
Two formal gas models for multi-agent sweeping and obstacle avoidance
FAABS'04 Proceedings of the Third international conference on Formal Approaches to Agent-Based Systems
International Journal of Bio-Inspired Computation
International Journal of Computer Applications in Technology
Artificial physics optimisation algorithm guided by diversity
International Journal of Computer Applications in Technology
Swarm robots search based on artificial physics optimisation algorithm
International Journal of Computing Science and Mathematics
The model of swarm robots search with local sense based on artificial physics optimisation
International Journal of Computing Science and Mathematics
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Artificial physics optimisation (APO) algorithm is a novel population-based stochastic algorithm based on physicomimetics framework for multidimensional search and optimisation. APO invokes a gravitational metaphor in which the force of gravity may be attractive or repulsive, the aggregate effect of which is to move individuals toward local and global optima. A proof of convergence is presented that reveals the conditions under which APO is guaranteed to converge. These convergence conditions indicate that some individuals have convergence behaviours whereas other individuals have divergent behaviours in APO system. According to the character, it can be proved that APO algorithm converge to the vicinity of global optimum with probability one based on the related knowledge of probability theory, which is proposed in brief. By regarding each individual|s position on each evolutionary step as a stochastic vector, APO algorithm determined by non-negative real parameter tuple {mi, w, G} is analysed using discrete-time linear system theory. The convergent condition of APO algorithm and corresponding parameter selection guidelines are derived. The simulation results show that the convergent condition is effective in guiding the parameter selection of APO algorithm and can help to explain why those parameters work well.