Parameter identification for elasto-plastic modelling of unsaturated soils from pressuremeter tests by parallel modified particle swarm optimization

2013 ◽  
Vol 48 ◽  
pp. 293-303 ◽  
Author(s):  
Youliang Zhang ◽  
Domenico Gallipoli ◽  
Charles Augarde
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Unsaturated Soils ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

scholarly journals Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage using Modified Particle Swarm Optimization

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 315
Author(s):  
Khubab Ahmed ◽  
Peng Yan ◽  
Su Li
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Model Identification ◽  
Particle Swarm ◽  
Identification Problem ◽  
Search Space ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Local Optima ◽  
Modified Particle Swarm Optimization

This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters; then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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