A novel levy flight trajectory-based salp swarm algorithm for photovoltaic parameters estimation

2021 ◽  
Vol 42 (8) ◽  
pp. 1841-1867
Author(s):  
Dallel Nasri ◽  
Diab Mokeddem ◽  
Bachir Bourouba ◽  
Jerome Bosche
Keyword(s):  
Parameters Estimation ◽  
Lévy Flight ◽  
Flight Trajectory ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Photovoltaic Parameters ◽  
Swarm Algorithm

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

2021 ◽  
pp. 1-12
Author(s):  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
High Dimensional ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

scholarly journals Estimation of Photovoltaic Cells Parameters Using Chaos Embedded Salp Swarm Algorithm

2020 ◽  
Vol 13 (6) ◽  
pp. 110-119
Author(s):  
Dallel Nasri ◽  
◽  
Diab Mokeddem ◽  
Bachir Bourouba ◽  
◽  
...  
Keyword(s):  
Solar Cells ◽  
High Performance ◽  
Photovoltaic Cells ◽  
Parameters Estimation ◽  
Accurate Estimation ◽  
Well Performance ◽  
Estimation Of Parameters ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Parameter Values

Solar photovoltaic (PV) systems have recently attracted researcher’s attention as a clean source of energy. Thus, the importance to design appropriately the photovoltaic cells highly raises. The main problems faced in the design process are first, the development of a useful model describing the characteristics of the current vs. voltage able to simulate the real solar cells behaviours and then, the precise estimation of photovoltaic cells parameter values. This paper employs an improved version of Salp Swarm Algorithm called Chaotic Salp Swarm Algorithm (CSSA) for the parameters estimation of solar cells in both single and double diode models. CSSA approach benefits from chaotic maps proprieties, and has the advantage of providing good equilibrium between exploration and exploitation mechanisms as well. Performance of the proposed CSSA is compared to fourteen known algorithms. Experimental results demonstrate that the proposed algorithm has the ability to find the optimal solutions with an accurate estimation of parameters for the courant vs voltage characteristics of real solar cell with high performance.

Lévy flight trajectory-based whale optimization algorithm for engineering optimization

2018 ◽  
Vol 35 (7) ◽  
pp. 2406-2428 ◽  
Author(s):  
Yongquan Zhou ◽  
Ying Ling ◽  
Qifang Luo
Keyword(s):  
Optimization Problems ◽  
Engineering Optimization ◽  
Lévy Flight ◽  
Flight Trajectory ◽  
Levy Flight ◽  
Content Type ◽  
Search Spaces ◽  
Engineering Design Problems ◽  
Whale Optimization ◽  
Engineering Optimization Problems

Purpose This paper aims to represent an improved whale optimization algorithm (WOA) based on a Lévy flight trajectory and called the LWOA algorithm to solve engineering optimization problems. The LWOA makes the WOA faster, more robust and significantly enhances the WOA. In the LWOA, the Lévy flight trajectory enhances the capability of jumping out of the local optima and is helpful for smoothly balancing exploration and exploitation of the WOA. It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Design/methodology/approach In this paper, an improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is represented to solve engineering optimization problems. Findings It has been successfully applied to five standard engineering optimization problems. The simulation results of the classical engineering design problems and real application exhibit the superiority of the LWOA algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic WOA algorithm or other available solutions. Originality value An improved WOA based on a Lévy flight trajectory and called the LWOA algorithm is first proposed.

scholarly journals Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Zhenrui Peng ◽  
Kangli Dong ◽  
Hong Yin ◽  
Yu Bai
Keyword(s):  
Dynamic Parameter ◽  
Benchmark Problems ◽  
Local Optimum ◽  
Value Functions ◽  
Lévy Flight ◽  
Step Size ◽  
Levy Flight ◽  
Artificial Fish Swarm Algorithm ◽  
Searching Strategy ◽  
Swarm Algorithm

Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of multidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on Lévy flight and firefly behavior is proposed. LFFSA incorporates the moving strategy of firefly algorithm into two behavior patterns of fish swarm, i.e., chasing behavior and preying behavior. Furthermore, Lévy flight is introduced into the searching strategy. To limit the search band, nonlinear view and step size based on dynamic parameter are considered. Finally, the proposed algorithm LFFSA is validated with several benchmark problems. Numerical results demonstrate that LFFSA has a better performance in convergence speed and optimization accuracy than the other test algorithms.

Sign in / Sign up

Export Citation Format

Share Document