scholarly journals Solving Cold-Standby Reliability-Redundancy Allocation Problems with Particle-Based Simplified Swarm Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chia-Ling Huang ◽  
Yunzhi Jiang ◽  
Wei-Chang Yeh
Keyword(s):  
Particle Swarm Optimization ◽  
System Reliability ◽  
State Of The Art ◽  
Series System ◽  
Swarm Optimization ◽  
Redundancy Allocation ◽  
System A ◽  
Cold Standby ◽  
Allocation Problems ◽  
Intelligence Technique

Particle swarm optimization (PSO) and simplified swarm optimization (SSO) are two of the state-of-the-art swarm intelligence technique that is widely utilized for optimization purposes. This paper describes a particle-based simplified swarm optimization (PSSO) procedure which combines the update mechanisms (UMs) of PSO and SSO to determine optimal system reliability for reliability-redundancy allocation problems (RRAPs) with cold-standby strategy while aimed at maximizing the system reliability. With comprehensive experimental test on the typical and famous four benchmarks of RRAP, PSSO is compared with other recently introduced algorithms in four different widely used systems, i.e., a series system, a series-parallel system, a complex (bridge) system, and an overspeed protection system for a gas turbine. Finally, the results of the experiments demonstrate that the PSSO can effectively solve the system of RRAP with cold-standby strategy and has good performance in the system reliability obtained although the best system reliability is not obtained in all four benchmarks.

scholarly journals Optimization for the Redundancy Allocation Problem of Reliability Using an Improved Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
H. Marouani
Keyword(s):  
Particle Swarm Optimization ◽  
System Reliability ◽  
Particle Swarm ◽  
Classical Particle ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Redundancy Allocation ◽  
Improved Particle Swarm Optimization ◽  
Efficiency And Effectiveness ◽  
In Series

This paper presents an enhanced and improved particle swarm optimization (PSO) approach to overcome reliability-redundancy allocation problems in series, series-parallel, and complex systems. The problems mentioned above can be solved by increasing the overall system reliability and minimizing the system cost, weight, and volume. To achieve this with these nonlinear constraints, an approach is developed based on PSO. In particular, the inertia and acceleration coefficients of the classical particle swarm algorithm are improved by considering a normal distribution for the coefficients. The new expressions can enhance the global search ability in the initial stage, restrain premature convergence, and enable the algorithm to focus on the local fine search in the later stage, and this can enhance the perfection of the optimization process. Illustrative examples are provided as proof of the efficiency and effectiveness of the proposed approach. Results show that the overall system reliability is far better when compared with that of some approaches developed in previous studies for all three tested cases.

scholarly journals Recursive Neural Networks Based on PSO for Image Parsing

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Guo-Rong Cai ◽  
Shui-Li Chen
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
State Of The Art ◽  
Pso Algorithm ◽  
Training Algorithm ◽  
Image Parsing ◽  
Swarm Optimization ◽  
Recursive Neural Networks ◽  
Parsing Algorithm

This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.

scholarly journals Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

2020 ◽  
Author(s):  
Charly Empereur-mot ◽  
Luca Pesce ◽  
Davide Bochicchio ◽  
Claudio Perego ◽  
Giovanni M. Pavan
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Structural Complexity ◽  
Coarse Grained ◽  
Complex Molecules ◽  
Swarm Optimization ◽  
Molecular Systems ◽  
Self Tuning ◽  
User Friendly ◽  
Python Package

We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default<br>settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.<br>Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force<br>fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.

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