scholarly journals Numerical study and parameter optimization of partial journal bearing using MOPSO algorithm

2019 ◽  
Vol 234 (1) ◽  
pp. 145-158
Author(s):  
SZ Mikaeeli ◽  
C Aghanajafi ◽  
P Akbarzadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Study ◽  
Particle Swarm ◽  
Journal Bearings ◽  
Fluid Film ◽  
Radial Clearance ◽  
Objective Functions ◽  
Governing Equations ◽  
Swarm Optimization ◽  
Multi Objective

In this paper, multi-objective particle swarm optimization method is developed for optimizing thermo-hydrodynamic journal bearings. This paper focuses on the use of multi-objective particle swarm optimization algorithm with a combination of the thermal hydrodynamic governing equations of the fluid film (i.e. momentum and energy equations) to optimize hydrodynamic partial pad journal bearings and compare with other articles. The governing equations are solved by the central difference method with a successive over-relaxation scheme and the backward difference with an iterative technique. In the paper, the lubricant viscosity changes with the temperature variation in whole fluid film. In this optimization, the bearing power loss, the minimum oil film thickness, and the maximum oil temperature are considered as objective functions and the radial clearance and length to diameter ratio are selected as design variables. The results of the objective functions are compared to other articles. Also, this study discusses the entropy and availability of two concentric cylinders with low curvature and constant wall temperature. Calculations showed that by increasing the Eckert number, the availability increases.

Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms

2016 ◽  
Vol 7 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Manjunath Patel G C ◽  
Prasad Krishna ◽  
Mahesh B. Parappagoudar ◽  
Pandu Ranga Vundavilli
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Squeeze Casting ◽  
Particle Swarm ◽  
Casting Process ◽  
Process Variable ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Squeeze Casting Process

The present work focuses on determining optimum squeeze casting process parameters using evolutionary algorithms. Evolutionary algorithms, such as genetic algorithm, particle swarm optimization, and multi objective particle swarm optimization based on crowing distance mechanism, have been used to determine the process variable combinations for the multiple objective functions. In multi-objective optimization, there are no single optimal process variable combination due to conflicting nature of objective functions. Four cases have been considered after assigning different combination of weights to the individual objective function based on the user importance. Confirmation tests have been conducted for the recommended process variable combinations obtained by genetic algorithm (GA), particle swarm optimization (PSO), and multiple objective particle swarm optimization based on crowing distance (MOPSO-CD). The performance of PSO is found to be comparable with that of GA for identifying optimal process variable combinations. However, PSO outperformed GA with regard to computation time.

Aerodynamic Shape Optimization Using Improved Territorial Particle Swarm Algorithm

2012 ◽  
Author(s):  
Amir Nejat ◽  
Pooya Mirzabeygi ◽  
Masoud Shariat-Panahi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Weighted Average ◽  
Particle Swarm Algorithm ◽  
Density Estimator ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective

In this paper, a new robust optimization technique with the ability of solving single and multi-objective constrained design optimization problems in aerodynamics is presented. This new technique is an improved Territorial Particle Swarm Optimization (TPSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively. The TPSO algorithm is developed to take into account multiple objective functions using a Pareto-Based approach. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed optimization benchmark functions and aerodynamic design problems. In final airfoil design obtained from the Multi Objective Territorial Particle Swarm Optimization algorithm, separation point is delayed to make the airfoil less susceptible to stall in high angle of attack conditions. The optimized airfoil also reveals an evident improvement over the test case airfoil across all objective functions presented.

Multi-Objective Comprehensive Charging/Discharging Scheduling Strategy for Electric Vehicles Based on the Improved Particle Swarm Optimization Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Baling Fang ◽  
Bo Li ◽  
Xingcheng Li ◽  
Yunzhen Jia ◽  
Wenzhe Xu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Vehicles ◽  
Distribution Network ◽  
Particle Swarm ◽  
Objective Functions ◽  
Swarm Optimization ◽  
System Load ◽  
Improved Particle Swarm Optimization ◽  
Scheduling Strategy ◽  
Multi Objective

To solve the problems that a large number of random and uncontrolled electric vehicles (EVs) connecting to the distribution network, resulting in a decrease in the performance and stability of the grid and high user costs, in this study, a multi-objective comprehensive charging/discharging scheduling strategy for EVs based on improved particle swarm optimization (IPSO) is proposed. In the distribution network, the minimum root-mean-square error and the minimum peak valley difference of system load are first designed as objective functions; on the user side, the lowest charge and discharge cost of electric vehicle users and the lowest battery loss cost are used as objective functions, then a multi-objective optimization scheduling model for EVs is established, and finally, the optimization through IPSO is performed. The simulation results show that the proposed method is effective, which enhances the peak regulating capacity of the power grid, and it optimizes the system load and reduces the user cost compared with the conventional methods.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

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