A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization

2013 ◽  
pp. 95-115 ◽  
Author(s):  
Daniel Leal Souza ◽  
Otávio Noura Teixeira ◽  
Dionne Cavalcante Monteiro ◽  
Roberto Célio Limão de Oliveira
Keyword(s):  
Engineering Optimization ◽  
Cuda Architecture

scholarly journals Constrained Engineering Optimization Algorithm Based on Elite Selection

2014 ◽  
Vol 8 (1) ◽  
pp. 85-103 ◽  
Author(s):  
Xuesong Yan ◽  
Wenjing Luo ◽  
Chengyu Hu ◽  
Hong Yao ◽  
Qinghua Wu
Keyword(s):  
Optimization Algorithm ◽  
Engineering Optimization ◽  
Elite Selection

scholarly journals Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1092
Author(s):  
Qing Duan ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Xingping Sun ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Chaotic Sequence ◽  
Engineering Optimization ◽  
Exploration And Exploitation ◽  
Salp Swarm Algorithm ◽  
Symmetric Perturbation ◽  
Swarm Algorithm ◽  
Search Capability ◽  
Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm(SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

Flip-teaching engineering optimization, electromagnetic product design, and nondestructive evaluation in a semester's course

2014 ◽  
Vol 23 (3) ◽  
pp. 374-382 ◽  
Author(s):  
Samuel Ratnajeevan H. Hoole ◽  
Sivamayam Sivasuthan ◽  
Victor U. Karthik ◽  
Paul Ratnamahilan P. Hoole
Keyword(s):  
Nondestructive Evaluation ◽  
Product Design ◽  
Engineering Optimization

scholarly journals Simplified Phasmatodea population evolution algorithm for optimization

2021 ◽  
Author(s):  
Pei-Cheng Song ◽  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan ◽  
Hongmei Yang
Keyword(s):  
Population Growth ◽  
Path Dependence ◽  
Optimization Problems ◽  
Stick Insect ◽  
Insect Population ◽  
Engineering Optimization ◽  
Population Evolution ◽  
Evolution Path ◽  
Evolution Characteristics ◽  
Evolution Algorithm

AbstractThis work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

Parameter Sensitivity Analysis of a Large-Scale System With Several Components Interacting in Series

1989 ◽  
Author(s):  
H. Torab
Keyword(s):  
Sensitivity Analysis ◽  
Large Scale ◽  
Optimization Problems ◽  
Parameter Sensitivity ◽  
Engineering Optimization ◽  
Parameter Sensitivity Analysis ◽  
Large Scale Systems ◽  
In Series ◽  
Special Case ◽  
Engineering Optimization Problems

Abstract Parameter sensitivity for large-scale systems that include several components which interface in series is presented. Large-scale systems can be divided into components or sub-systems to avoid excessive calculations in determining their optimum design. Model Coordination Method of Decomposition (MCMD) is one of the most commonly used methods to solve large-scale engineering optimization problems. In the Model Coordination Method of Decomposition, the vector of coordinating variables can be partitioned into two sub-vectors for systems with several components interacting in series. The first sub-vector consists of those variables that are common among all or most of the elements. The other sub-vector consists of those variables that are common between only two components that are in series. This study focuses on a parameter sensitivity analysis for this special case using MCMD.

scholarly journals A Gradient Taguchi Method for Engineering Optimization

2017 ◽  
Vol 241 ◽  
pp. 012022
Author(s):  
Shun-Fa Hwang ◽  
Jen-Chih Wu ◽  
Rong-Song He
Keyword(s):  
Taguchi Method ◽  
Engineering Optimization
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