Particle Swarm Optimization Method for Panel Layout

2010 ◽  
Vol 450 ◽  
pp. 308-311 ◽  
Author(s):  
Yu Qing Xu ◽  
Zhi Yang ◽  
Qing Xin Meng
Keyword(s):  
Particle Swarm Optimization ◽  
Nuclear Power ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Operational Conditions ◽  
Frequency Sequence

Panel layout in control room of nuclear power plant requires satisfying layout principles and requirement for multiple operational conditions. Traditional panel layout methods are difficult to integrate these principles; the layout scheme heavily depends on designers’ individual judgments and experience. So the randomness of layout scheme is hard to overcome. Particle swarm optimization is a global efficient solution to complex optimization problems with multi-constraints. First, the operational efficiency is selected as optimization object. Second, objective function is constructed according to the evaluating indexes which include operation frequency, sequence and importance of components. Third, Particle swarm algorithm was used to optimize the layout scheme. A case study of layout optimization design is conducted to illustrate the proposed method.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

Shape Optimization Design of Gravity Buttress of Arch Dam Based on Asynchronous Particle Swarm Optimization Method

2014 ◽  
Vol 662 ◽  
pp. 160-163
Author(s):  
Lei Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Design Method ◽  
Particle Swarm ◽  
Optimization Method ◽  
Arch Dam ◽  
Swarm Optimization ◽  
Safety Coefficient ◽  
Parametric Description ◽  
Particle Swarm Optimization Method

The optimization design method was rarely used to design the gravity buttress of arch dam in the past. With this in mind, the parametric description of gravity buttress is given, and the auto-calculation of its exerting loads and the safety coefficient of anti-slide stability are realized subsequently. Then, the optimization design model of gravity buttress and the procedures of optimization design are presented using the asynchronous particle swarm optimization method. Finally, ODGB software, which is short for Optimization Design of Gravity Buttress software, is developed and verified.

Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 18-29
Author(s):  
Rongrong Li ◽  
Linrun Qiu ◽  
Dongbo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Bottom Layer ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Elite Group ◽  
Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization

2012 ◽  
Vol 201-202 ◽  
pp. 283-286
Author(s):  
Chen Yang Chang ◽  
Jing Mei Zhai ◽  
Qin Xiang Xia ◽  
Bin Cai
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Structure Optimization ◽  
Design Parameters ◽  
Support Vector ◽  
Swarm Optimization ◽  
Multi Objective

Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particle Swam Optimization (PSO) algorithm. A slip cover at medical treatment was adopted as an example to illustrate this methodology. Appropriate design parameters were selected through the orthogonal experiment combined with ANSYS. The results show this methodology is accurate and feasible, which provides an effective strategy to solve complex optimization problems.

scholarly journals Improvement of Imperialist Competitive Algorithm based on the Cosine Similarity Criterion of Neighboring Objects

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Maryam Houtinezhad ◽  
Hamid Reza Ghaffary
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Imperialist Competitive Algorithm ◽  
Cosine Similarity ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Competitive Algorithm ◽  
Combined Algorithm

The goal of optimizing the best acceptable answer is according to the limitations and needs of the problem. For a problem, there are several different answers that are defined to compare them and select an optimal answer; a function is called a target function. The choice of this function depends on the nature of the problem. Sometimes several goals are together optimized; such optimization problems are called multi-objective issues. One way to deal with such problems is to form a new objective function in the form of a linear combination of the main objective functions. In the proposed approach, in order to increase the ability to discover new position in the Imperialist Competitive Algorithm (ICA), its operators are combined with the particle swarm optimization. The colonial competition optimization algorithm has the ability to search global and has a fast convergence rate, and the particle swarm algorithm added to it increases the accuracy of searches. In this approach, the cosine similarity of the neighboring countries is measured by the nearest colonies of an imperialist and closest competitor country. In the proposed method, by balancing the global and local search, a method for improving the performance of the two algorithms is presented. The simulation results of the combined algorithm have been evaluated with some of the benchmark functions. Comparison of the results has been evaluated with respect to metaheuristic algorithms such as Differential Evolution (DE), Ant Lion Optimizer (ALO), ICA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

Hypothesis testing-based adaptive PSO

2014 ◽  
Vol 12 (1) ◽  
pp. 89-101 ◽  
Author(s):  
Yanxia Sun ◽  
Karim Djouani ◽  
Barend Jacobus van Wyk ◽  
Zenghui Wang ◽  
Patrick Siarry
Keyword(s):  
Particle Swarm Optimization ◽  
Hypothesis Testing ◽  
Local Minimum ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Content Type ◽  
Adaptive Pso

Purpose – In this paper, a new method to improve the performance of particle swarm optimization is proposed. Design/methodology/approach – This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added. Findings – The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality. Originality/value – This paper proposes an adaptive particle swarm optimization method (APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.

Application of Particle Swarm Optimization in Cylinder Helical HGearH Multi-Objective Design

2011 ◽  
Vol 474-476 ◽  
pp. 2229-2233
Author(s):  
Yuan Bin Mo ◽  
Ji Zhong Liu ◽  
Bao Lei Wang ◽  
Wei Min Wan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

Cylinder helical gGear is widely used in industry. Multi-objective optimization design of the component is often met in its different application sSituation. In this paper a novel multi-objective optimization method based on Particle Swarm Optimization (PSO) algorithm is designed for applying to solve this kind of problem. A paradigm depicted in the paper shows the algorithm is practical.

Sign in / Sign up

Export Citation Format

Share Document