scholarly journals Optimization of elderly nutrition needs using PSO algorithm: A case study at POSBINDU PTM Sejahtera

2021 ◽  
Vol 1098 (6) ◽  
pp. 062009
Author(s):  
R Rizqullah ◽  
S D H Permana ◽  
Y Yaddarabullah
Keyword(s):  
Pso Algorithm ◽  
Elderly Nutrition

Reliability-Based Analysis and Optimization of the Gravity Balancing Performance of Spring-Articulated Serial Robots with Uncertainties

2021 ◽  
pp. 1-32
Author(s):  
Vu Linh Nguyen ◽  
Chin-Hsing Kuo ◽  
Po Ting Lin
Keyword(s):  
Pso Algorithm ◽  
Reliability Based Design Optimization ◽  
Swarm Optimization ◽  
Reliability Based Design ◽  
Serial Robots ◽  
Simulation Based ◽  
Serial Robot ◽  
Simulation Results ◽  
Uncertainty Level

Abstract This article proposes a method for analyzing the gravity balancing reliability of spring-articulated serial robots with uncertainties. Gravity balancing reliability is defined as the probability that the torque reduction ratio (the ratio of the balanced torque to the unbalanced torque) is less than a specified threshold. The reliability analysis is performed by exploiting a Monte Carlo simulation (MCS) with consideration of the uncertainties in the link dimensions, masses, and compliance parameters. The gravity balancing begins with a simulation-based analysis of the gravitational torques of a typical serial robot. Based on the simulation results, a gravity balancing design for the robot using mechanical springs is realized. A reliability-based design optimization (RBDO) method is also developed to seek a reliable and robust design for maximized balancing performance under a prescribed uncertainty level. The RBDO is formulated with consideration of a probabilistic reliability constraint and solved by using a particle swarm optimization (PSO) algorithm. A numerical example is provided to illustrate the gravity balancing performance and reliability of a robot with uncertainties. A sensitivity analysis of the balancing design is also performed. Lastly, the effectiveness of the RBDO method is demonstrated through a case study in which the balancing performance and reliability of a robot with uncertainties are improved with the proposed method.

Optimal design of photovoltaic solar systems considering shading effect and hourly radiation using a modified PSO algorithm

SIMULATION ◽  
2019 ◽  
Vol 95 (10) ◽  
pp. 931-939 ◽  
Author(s):  
Mohammad Hossein Shams ◽  
Mohsen Kia ◽  
Alireza Heidari ◽  
Daming Zhang
Keyword(s):  
Energy Demand ◽  
Pso Algorithm ◽  
Minimum Area ◽  
Solar Systems ◽  
Modified Particle Swarm Optimization ◽  
Pv Systems ◽  
Roof Area ◽  
Solar Field ◽  
The Given

Regarding the significant potential of solar energy in Iran, implementation of optimally designed photovoltaic (PV) systems can be effective. Hence, this study proposes two objective functions: first, the maximum possible output energy for a given area and, second, the minimum area receiving a given yearly energy from PV fixed collectors in a solar field, both of which are calculated. In addition, the shading and masking effects are considered in the calculations. A modified particle swarm optimization (MPSO) algorithm is used to solve the optimization problem. The case study of this article is a shopping center in Isfahan-Iran (latitude 32.5°N) with the minimum yearly energy demand of 171 MWh and the 5000 m2 roof area. To evaluate the yearly energy, the calculated hourly radiation approach is applied to the case study. The results show that the maximum possible generated energy is 881 MWh/year for the given area. In addition, to provide the minimum demand, 720 m2 area of roof is needed. To verify the effectiveness of the proposed MPSO, the results are compared with those of obtained by the relevant commercial software.

scholarly journals A Method for Consensus Reaching in Product Kansei Evaluation Using Advanced Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yan-pu Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Product Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Design Evaluation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Design Alternatives ◽  
Consensus Reaching

Consumers’ opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers’ preference. However, how to identify and improve the reliability of consumers’ Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers’ opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers’ opinions. Furthermore, the process of the proposed method is presented and the details are illustrated using an example of electronic scooter design evaluation. The case study reveals that the proposed method is promising for reaching a consensus through searching optimal solutions by PSO and improving the reliability of consumers’ evaluation opinions toward design alternatives according to Kansei indexes.

scholarly journals Quantitative Evaluation of the “Non-Enclosed” Microseismic Array: A Case Study in a Deeply Buried Twin-Tube Tunnel

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 2006 ◽  
Author(s):  
Hang Zhang ◽  
Chunchi Ma ◽  
Tianbin Li
Keyword(s):  
High Stress ◽  
Pso Algorithm ◽  
Maximum Error ◽  
Microseismic Monitoring ◽  
Source Location ◽  
Tunnel Engineering ◽  
Underground Engineering ◽  
Location Accuracy ◽  
Tube Arrays

The high-stress hazards of underground engineering have stimulated the exploration of microseismic monitoring and early warning methods. To achieve a good monitoring effect, the monitoring object is usually enclosed by a microseismic array (sensor array) (e.g., slope engineering, etc.). However, some characteristics of a buried tunnel, including “linear”, “deep-buried”, and “long”, make it difficult to deploy a reasonable microseismic array, which leads to the microseismic array being non-enclosed for the monitoring object. Application of the non-enclosed microseismic array yields decreases the accuracy of the source location. To solve the problem wisely, this paper deals with the feasibility of non-enclosed microseismic arrays (axial-extended, lateral-extended, and twin-tube arrays) by introducing a quantitative method. To this end, an optimized microseismic array with the best source location accuracy for a twin-tube expressway tunnel is proposed. The obtained results reveal that the non-enclosed microseismic arrays, which are unavoidable in expressway tunnel engineering, do not introduce errors but reduce the ability to resist them. Further, the twin-tube array achieves a better source location accuracy than the axial and lateral-extended arrays. In the application of the source location based on the particle swarm optimization (PSO) algorithm to the twin-tube array, microseismic events, which cluster in the rockburst section, are wholly gathered, and the maximum error is reduced by about 30–50 m, indicating its greater feasibility with respect to the single-tube array.

scholarly journals Application of an improved PSO algorithm mixed BFO algorithm in optimal scheduling of hydropower station

2018 ◽  
Vol 246 ◽  
pp. 01044
Author(s):  
Yibo Zou ◽  
Mo Li ◽  
Xiaogang Xiao
Keyword(s):  
Strong Dependence ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Local Extremum ◽  
Optimal Scheduling ◽  
Hydropower Station ◽  
Scheduling Model ◽  
Multi Stage ◽  
Improved Pso

The optimal scheduling of hydropower station is a constrained strong, nonlinear and multi-stage combinatorial optimization. Aiming at this issue, this paper analyses the shortcomings of previous PSO algorithm in hydropower station optimal scheduling model, and presents an improved PSO algorithm for hybrid BFO algorithm, which overcomes the problem that the PSO algorithm is easy to fall into local extremum and has strong dependence on parameters. A case study of a short-term scheduling period of a hydropower station is used to compare the improved PSO algorithm mixed BFO algorithm with previous PSO algorithm. The results show that the improved PSO algorithm can converge to the global optimal solution more accurately. Therefore, it provides a new method for solving the optimal scheduling model of hydropower station.

A novel algorithm for solving resource-constrained project scheduling problems: a case study

2019 ◽  
Vol 16 (2) ◽  
pp. 194-215 ◽  
Author(s):  
Mahmood Kasravi ◽  
Amin Mahmoudi ◽  
Mohammad Reza Feylizadeh
Keyword(s):  
Project Scheduling ◽  
Hybrid Algorithm ◽  
Pso Algorithm ◽  
Project Planning ◽  
Heuristic Methods ◽  
Scheduling Problems ◽  
Resource Constrained ◽  
Content Type ◽  
Resource Constrained Project Scheduling

PurposeConstruction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.Design/methodology/approachDue to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.FindingsAccording to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.Practical implicationsICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.Originality/valueIn this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

Multi-Thread Optimization for the Calibration of Microscopic Traffic Simulation Model

2018 ◽  
Vol 2672 (20) ◽  
pp. 98-109 ◽  
Author(s):  
Zenghao Hou ◽  
Joyoung Lee
Keyword(s):  
Monte Carlo ◽  
Simulation Model ◽  
Traffic Simulation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Approach ◽  
Microscopic Traffic Simulation ◽  
Quasi Monte Carlo ◽  
Study Results

This paper proposes an innovative multi-thread stochastic optimization approach for the calibration of microscopic traffic simulation models. Combining Quasi-Monte Carlo (QMC) sampling and the Particle Swarm Optimization (PSO) algorithm, the proposed approach, namely the Quasi-Monte Carlo Particle Swarm (QPS) calibration method, is designed to boost the searching process without prejudice to the calibration accuracy. Given the search space constructed by the combinations of simulation parameters, the QMC sampling technique filters the searching space, followed by the multi-thread optimization through the PSO algorithm. A systematic framework for the implementation of the QPS QMC-initialized PSO method is developed and applied for a case study dealing with a large-scale simulation model covering a 6-mile stretch of Interstate Highway 66 (I-66) in Fairfax, Virginia. The case study results prove that the proposed QPS method outperforms other methods utilizing Genetic Algorithm and Latin Hypercube Sampling in achieving faster convergence to obtain an optimal calibration parameter set.

Enhanced OPF for DG Penetrated Power System Network under Variable Load Conditions

2014 ◽  
Vol 984-985 ◽  
pp. 1301-1305
Author(s):  
P. Sivakumar ◽  
Arumugam Rajapandiyan
Keyword(s):  
Power Systems ◽  
New England ◽  
Distributed Generation ◽  
Renewable Energy Sources ◽  
Pso Algorithm ◽  
Test System ◽  
Variable Load ◽  
Swarm Optimization ◽  
Distributed Generators

In modern power systems, distributed generation turns out to be progressively significant. Conversely, the growing utilize of distributed generators origins the concerns on the growing system hazard owing to their probable breakdown or unruly power productivity based on such renewable energy sources as wind and the sun. Power contribution at the required proportion by the grids is the chief performance consideration which depends upon the penetration of distributed generation and the accessibility of conventional sources during the load transform. In this paper, the projected approach is that the essential load power is divided evenly between the grids composed of Distributed Generation (DG) units and the utility based on the PSO algorithm during the load transform. A case study is carried out based on the New England test system (10-Generator-39-Bus) as a standard by using Particle swarm optimization (PSO) algorithm.

Reservoir quality management with CE-QUAL-W2/ANN surrogate model and PSO algorithm (case study: Pishin Dam, Iran)

2021 ◽  
Vol 14 (5) ◽  
Author(s):  
Faranak Khodabandeh ◽  
Mohsen Dehghani Darmian ◽  
Mehdi Azhdary Moghaddam ◽  
Seyed Arman Hashemi Monfared
Keyword(s):  
Quality Management ◽  
Surrogate Model ◽  
Pso Algorithm ◽  
Reservoir Quality
Sign in / Sign up

Export Citation Format

Share Document