Accelerating Parameter Estimation for Photovoltaic Models via Parallel Particle Swarm Optimization
10
Citation
14
Reference
10
Related Paper
Citation Trend
Abstract:
Bio-inspired metaheuristic algorithms have been widely proposed to estimate parameters of photovoltaic (PV) models in recent years due to its ability to handle nonlinear functions regardless of the derivatives information. However, these algorithms normally utilize multiple agents/particles in the search process, and it takes much time to search the possible solutions in the whole search domain by sequential computing devices. This paper proposes parallel particle swarm optimization (PPSO) method to extract and estimate the parameters of a PV model. The algorithm is implemented in OpenCL and is executed on Nvidia multi-core GPUs. From the simulation results, it is observed that the proposed method is capable of accelerating the computational speed with the same accuracy in comparison to sequential particle swarm optimization (PSO).The swarm behaviour can be fully determined by attractants (food pieces) which change the directions of swarm propagation. If we assume that at each time step the swarm can find out not more than p – 1 attractants, then the swarm behaviour can be coded by p-adic integers. The main task of any swarm is to logistically optimize the road system connecting the reachable attractants. In the meanwhile, the transporting network of the swarm has loops (circles) and permanently changes, e.g. the swarm occupies some attractants and leaves the others. However, this complex dynamics can be effectively coded by p-adic integers. This allows us to represent the swarm behaviour as a calculation on p-adic valued strings.
Swarm intelligence
Cite
Citations (3)
Natural swarms can be formed by various creatures. The swarms can conduct demanded behaviors to adapt to their living environments, such as passing through harsh terrains and protecting each other from predators. At micrometer and nanometer scales, formation of a swarm pattern relies on the physical or chemical interactions between the agents owing to the absence of an on-board device. Independent pattern formation of different swarms, especially under the same input, is a more challenging task. In this work, a swarm of nickel nanorods is proposed and by exploiting its different behavior with the nanoparticle swarm, independent pattern formation of diverse microrobotic swarms under the same environment can be conducted. A mathematical model for the nanorod swarm is constructed, and the mechanism is illustrated. Two-region pattern changing of the nanorod swarm is discovered and compared with the one-region property of the nanoparticle swarm. Experimental characterization of the nanorod swarm pattern is conducted to prove the concept and validate the effectiveness of the theoretical analysis. Furthermore, independent pattern formation of different microrobotic swarms was demonstrated. The pattern of the nanorod swarm could be adjusted while the other swarm was kept unchanged. Simultaneous pattern changing of two swarms was achieved as well. As a fundamental research on the microrobotic swarm, this work presents how the nanoscale magnetic anisotropy of building agents affects their macroscopic swarm behaviors and promotes further development on the independent control of microrobotic swarms under a global field input.
Nanorod
Pattern Formation
Swarm Robotics
Swarm intelligence
Cite
Citations (41)
Battlefield
Swarm Robotics
Swarm intelligence
Electromagnetic environment
Cite
Citations (0)
An M-Member swarm system with energetic behavior is studied in this paper. A new type of swarm controller is developed such that a swarm can follow a desired trajectory with different swarm temperatures and potential energy values. The temperature allows the internal kinetic energy of the swarm to be modulated. As the temperature increases the motion of the swarm becomes more energetic and areas are covered by the swarm in less time. The potential energy controls the size of the swarm and also provides new guarantees of energetic swarm cohesion. Simulation is used to validate the results and to demonstrate the new approach.
Cohesion (chemistry)
Swarm Robotics
Cite
Citations (2)
Cite
Citations (0)
Swarm intelligence
Swarm Robotics
Cite
Citations (2)
Insects and fish, which make swarm in nature, are simple organisms as individuals. However, such organisms behave in an orderly manner throughout the swarm by cooperating with each other using simple rules. In this paper, swarm model with variable parameters is proposed. Simulation results showed parameters that allowed the swarm to pass through the confined environment quickly. In case of the variable avoidance speed coefficient and the high-speed coefficient, the effect on the swarm was significant. On the other hand, in case of the variable low-speed coefficient, there was negligible effect on the swarm.
Swarm Robotics
Swarm intelligence
Cite
Citations (0)
This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task.
The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.
Swarm Robotics
Swarm intelligence
Resilience
Cite
Citations (5)
This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic policy evolution to provide agility within the swarm, enabling each swarm member to select the most appropriate mobility policy for the environment given the swarm's abilities. The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. These methods are either human-designed, which the swarm adopts when most appropriate, or are novel hybridisations that the swarm creates for the problem. This paper also tests the swarm with individuals being removed during deployment. It is found that the swarm is immune to most alterations, though removal of specialised members of the heterogeneous swarm leads to temporary failure. The swarm evolution can then correct this failure by adjusting the swarm behaviour.
Swarm Robotics
Swarm intelligence
Cite
Citations (9)
This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task.
The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.
Swarm Robotics
Swarm intelligence
Resilience
Cite
Citations (7)