An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Distributed, Physics-Based Control of Swarms of Vehicles
Autonomous Robots
A global optimization based on physicomimetics framework
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Integral Particle Swarm Optimization with Dispersed Accelerator Information
Fundamenta Informaticae - Swarm Intelligence
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
Artificial physics optimisation: a brief survey
International Journal of Bio-Inspired Computation
A Hybrid Vector Artificial Physics Optimization with One-Dimensional Search Method
CASON '10 Proceedings of the 2010 International Conference on Computational Aspects of Social Networks
Bat algorithm for multi-objective optimisation
International Journal of Bio-Inspired Computation
A novel quantum inspired cuckoo search for knapsack problems
International Journal of Bio-Inspired Computation
The convergence analysis of artificial physics optimisation algorithm
International Journal of Intelligent Information and Database Systems
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
APOA with parabola model for directing orbits of chaotic systems
International Journal of Bio-Inspired Computation
Artificial plant optimisation algorithm with three-period photosynthesis
International Journal of Bio-Inspired Computation
Group search optimiser: a brief survey
International Journal of Computing Science and Mathematics
Hybrid ABC/PSO to solve travelling salesman problem
International Journal of Computing Science and Mathematics
Using Watts-Strogatz particle swarm optimisation to solve direct orbits of chaotic systems
International Journal of Computing Science and Mathematics
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The gravitational constant G is a particularly important parameter in artificial physics optimisation (APO) because it influences the algorithm's convergence. APO is a population-based heuristic whose swarm at each step can be divided into two distinct subsets: a divergent subset, and a convergent subset, the former containing all individuals exhibiting divergent behaviour, and the latter all others exhibiting convergent behaviour. How APO's population is apportioned between the divergent and convergent subsets is largely determined by the value of G. Two strategies for assigning its value were studied: a constant G, and an adaptive G. The disadvantage of the constant G case is mitigated by adaptive G by tuning the swarm's distribution between the two subsets. These strategies for selecting G were tested against several benchmark functions, and the results show that APO with an adaptive G outperforms APO with a constant G.