Particle Swarm Optimization for Ship Degaussing Coils Calibration

2012 ◽  
Vol 182-183 ◽  
pp. 1446-1451
Author(s):  
Ming Ming Yang ◽  
Da Ming Liu ◽  
Li Ting Lian
Keyword(s):  
Magnetic Field ◽  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Scale Factors ◽  
Single Objective

In this paper, we deal with the problem of the ship degaussing coils optimal calibration by a linearly decreasing weight particle swarm optimization (LDW-PSO). Taking the ship’s magnetic field and its gradient reduction into account, the problem is treated as a multi-objective optimization problem. First a set of scale factors are calculated by LDW-PSO to scale the two kinds of objective function, then the multi-objective optimization problem is transformed to a single objective optimization problem via a set of proper weights, and the problem is solved by LDW-PSO finally. A typical ship degaussing system is applied to test the method’s validity, and the results are good.

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

2014 ◽  
Vol 971-973 ◽  
pp. 1242-1246
Author(s):  
Tie Jun Chen ◽  
Yan Ling Zheng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Grinding Process ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Simulation Results

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

scholarly journals Protein Structure Refinement Using Multi-Objective Particle Swarm Optimization with Decomposition Strategy

2021 ◽  
Vol 22 (9) ◽  
pp. 4408
Author(s):  
Cheng-Peng Zhou ◽  
Di Wang ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Protein Structure ◽  
Optimization Problem ◽  
Structure Refinement ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Energy Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Protein Structure Refinement

Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with lower energy. Considering that a single energy function could not reflect the accurate energy landscape of all the proteins, our previous AIR 1.0 pipeline uses multiple energy functions to realize a multi-objectives particle swarm optimization-based model refinement. It is expected to provide a general balanced conformation search protocol guided from different energy evaluations. However, AIR 1.0 solves the multi-objective optimization problem as a whole, which could not result in good solution diversity and convergence on some targets. In this study, we report a decomposition-based method AIR 2.0, which is an updated version of AIR, for protein structure refinement. AIR 2.0 decomposes a multi-objective optimization problem into a number of subproblems and optimizes them simultaneously using particle swarm optimization algorithm. The solutions yielded by AIR 2.0 show better convergence and diversity compared to its previous version, which increases the possibilities of digging out better structure conformations. The experimental results on CASP13 refinement benchmark targets and blind tests in CASP 14 demonstrate the efficacy of AIR 2.0.

Modified Particle Swarm Optimization Algorithm for Multi-Objective Optimization Design of Hybrid Journal Bearings

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Chia-Wen Chan
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Design ◽  
High Efficiency ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Modified Particle Swarm Optimization

The objective of design optimization is to determine the design that minimizes the objective function by changing design variables and satisfying design constraints. During multi-objective optimization, which has been widely applied to improve bearing designs, designers must consider several design criteria or objective functions simultaneously. The particle swarm optimization (PSO) method is known for its simple implementation and high efficiency in solving multifactor but single-objective optimization problems. This paper introduces a new multi-objective algorithm (MOA) based on the PSO and Pareto methods that can greatly reduce the number of objective function calls when a suitable swarm size is set.

A hybrid method of multi-objective particle swarm optimization and k-means clustering and its application to modal parameter estimation in the time–frequency domain

2019 ◽  
Vol 26 (9-10) ◽  
pp. 769-778
Author(s):  
Kai Yang ◽  
Kaiping Yu ◽  
Hui Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Method ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Modal Parameters ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Multi Objective ◽  
External Archive

Modal parameters provide an insight into the dynamical properties of structures. In the time–frequency domain–based methods, time–frequency ridges contain crucial information on the characteristics of multicomponent signals, and manually extracting time–frequency ridges is a huge burden, especially when long-time time-varying modal parameters are focused on. In this study, time–frequency ridge extraction is converted into a multi-objective optimization problem, and a new hybrid method of multi-objective particle swarm optimization and k-means clustering is proposed to solve such a multi-objective optimization problem. In the hybrid method, the particle swarm is partitioned into sub-swarms by k-means clustering, and the sub-swarms are used to search new solutions for updating a finite-sized external archive, which is used as the exclusive centroids of the k-means clustering. Simultaneously, the finite-sized external archive serves as global best positions of sub-swarms. Both simulated and experimental cases are applied to validate the hybrid method. With the aid of the hybrid method, the influence of varying temperatures on modal parameters of a column beam is experimentally analyzed in detail.

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