Introduction to Continuous Optimization

2021 ◽  
Author(s):  
Roman A. Polyak
Keyword(s):  
Continuous Optimization

H-polytope decomposition-based algorithm for continuous optimization

2021 ◽  
Vol 558 ◽  
pp. 50-75
Author(s):  
Ghazaleh Khodabandelou ◽  
Amir Nakib
Keyword(s):  
Continuous Optimization

scholarly journals Graph based discrete optimization in structural dynamics

2014 ◽  
Vol 62 (1) ◽  
pp. 91-102
Author(s):  
B. Blachowski ◽  
W. Gutkowski
Keyword(s):  
Structural Optimization ◽  
Minimum Weight ◽  
Continuous Optimization ◽  
Graph Representation ◽  
Wind Loading ◽  
Transmission Tower ◽  
Simple Method ◽  
Deterministic Dynamic ◽  
Starting Point ◽  
Discrete Values

Abstract In this study, a relatively simple method of discrete structural optimization with dynamic loads is presented. It is based on a tree graph, representing discrete values of the structural weight. In practical design, the number of such values may be very large. This is because they are equal to the combination numbers, arising from numbers of structural members and prefabricated elements. The starting point of the method is the weight obtained from continuous optimization, which is assumed to be the lower bound of all possible discrete weights. Applying the graph, it is possible to find a set of weights close to the continuous solution. The smallest of these values, fulfilling constraints, is assumed to be the discrete minimum weight solution. Constraints can be imposed on stresses, displacements and accelerations. The short outline of the method is presented in Sec. 2. The idea of discrete structural optimization by means of graphs. The knowledge needed to apply the method is limited to the FEM and graph representation. The paper is illustrated with two examples. The first one deals with a transmission tower subjected to stochastic wind loading. The second one with a composite floor subjected to deterministic dynamic forces, coming from the synchronized crowd activities, like dance or aerobic.

scholarly journals An enhanced chimp optimization algorithm for continuous optimization domains

2021 ◽  
Author(s):  
Heming Jia ◽  
Kangjian Sun ◽  
Wanying Zhang ◽  
Xin Leng
Keyword(s):  
Social Status ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Rank Correlation ◽  
Function Optimization ◽  
Local Optimum ◽  
Hunting Behavior ◽  
Spearman’S Rank Correlation ◽  
Engineering Design Problems ◽  
Application Fields

AbstractChimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems

2021 ◽  
Author(s):  
Kayvan Asghari ◽  
Mohammad Masdari ◽  
Farhad Soleimanian Gharehchopogh ◽  
Rahim Saneifard
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Gray Wolf ◽  
Continuous Optimization Problems

Three modified versions of differential evolution algorithm for continuous optimization

2010 ◽  
Vol 15 (4) ◽  
pp. 803-830 ◽  
Author(s):  
Morteza Alinia Ahandani ◽  
Naser Pourqorban Shirjoposh ◽  
Reza Banimahd
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
Evolution Algorithm

A unified ant colony optimization algorithm for continuous optimization

2014 ◽  
Vol 234 (3) ◽  
pp. 597-609 ◽  
Author(s):  
Tianjun Liao ◽  
Thomas Stützle ◽  
Marco A. Montes de Oca ◽  
Marco Dorigo
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm
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