Connection Between Continuous Optimization and Turán Densities of Non-uniform Hypergraphs

In this paper, we consider the problem of constructing hypercycle systems of 5-cycles in complete 3-uniform hypergraphs. A hypercycle system C(r,k,v) of order v is a collection of r-uniform k-cycles on a v-element vertex set, such that each r-element subset is an edge in precisely one of those k-cycles. We present cyclic hypercycle systems C(3,5,v) of orders v=25,26,31,35,37,41,46,47,55,56, a highly symmetric construction for v=40, and cyclic 2-split constructions of orders 32,40,50,52. As a consequence, all orders v≤60 permitted by the divisibility conditions admit a C(3,5,v) system. New recursive constructions are also introduced.

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A hybrid optimizer based on backtracking search and differential evolution for continuous optimization

Journal of Experimental & Theoretical Artificial Intelligence ◽

10.1080/0952813x.2021.1872109 ◽

2021 ◽

pp. 1-31

Author(s):

Yiğit Çağatay Kuyu ◽

Enrique Onieva ◽

Pedro Lopez-Garcia

Keyword(s):

Differential Evolution ◽

Continuous Optimization ◽

Backtracking Search

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Graph based discrete optimization in structural dynamics

Bulletin of the Polish Academy of Sciences Technical Sciences ◽

10.2478/bpasts-2014-0011 ◽

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.

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An enhanced chimp optimization algorithm for continuous optimization domains

Complex & Intelligent Systems ◽

10.1007/s40747-021-00346-5 ◽

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.

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