Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
Using Artificial Physics to Control Agents
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Distributed, Physics-Based Control of Swarms of Vehicles
Autonomous Robots
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
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Tackling magnetoencephalography with particle swarm optimization
International Journal of Bio-Inspired Computation
The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm
International Journal of Bio-Inspired Computation
Parallel asynchronous control strategy for target search with swarm robots
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
A formal analysis of potential energy in a multi-agent system
FAABS'04 Proceedings of the Third international conference on Formal Approaches to Agent-Based Systems
Artificial physics optimisation: a brief survey
International Journal of Bio-Inspired Computation
A novel constraint multi-objective artificial physics optimisation algorithm and its convergence
International Journal of Innovative Computing and Applications
The convergence analysis of artificial physics optimisation algorithm
International Journal of Intelligent Information and Database 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
Hybrid ABC/PSO to solve travelling salesman problem
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 an optimisation algorithm based on physicomimetics framework. Driven by virtual force, a population of sample individuals searches a global optimum in the problem space. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimised. It is an important parameter to influence the performance of APO algorithm. Therefore, in this paper, the authors make a study on the selection principle of mass on numerical optimisation problems. According to the curvilinear style of the mass functions, they are classified into three different types of curvilinear functions: convex function, linear function and concave function. To make a deep insight, several versions of APO algorithm with different mass functions are used to solve two type benchmarks: unimodal and multimodal functions. Simulation results show the mass functions with concave curve may generally obtain the satisfied solution within the allowed iterations. In addition, the performance of APO algorithm is compared with that of the modified electromagnetism-like (EM), differential evolution (DE), evolutionary algorithm (EA) and particle swarm optimisation (PSO) for multidimensional numeric benchmarks. The simulation results show that APO algorithm is competitive.