Study on the Development of Complex Network for Evolutionary and Swarm Based Algorithms

Citation data:

Advances in Soft Computing, ISSN: 0302-9743, Vol: 10062 LNAI, Page: 151-161

Publication Year:
2017

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Repository URL:
http://publikace.k.utb.cz/handle/10563/1007478, http://hdl.handle.net/10563/1007478
DOI:
10.1007/978-3-319-62428-0_12
Author(s):
Šenkeřík, Roman, Zelinka, Ivan, Pluháček, Michal, Viktorin, Adam
Publisher(s):
Springer Nature, Springer Verlag
Tags:
Mathematics, Computer Science, Analysis, Complex networks, Differential evolution, Graphs, PSO
book chapter description
This contribution deals with the hybridization of complex network frameworks and metaheuristic algorithms. The population is visualized as an evolving complex network that exhibits non-trivial features. It briefly investigates the time and structure development of a complex network within a run of selected metaheuristic algorithms – i.e. PSO and Differential Evolution (DE). Two different approaches for the construction of complex networks are presented herein. It also briefly discusses the possible utilization of complex network attributes. These attributes include an adjacency graph that depicts interconnectivity, while centralities provide an overview of convergence and stagnation, and clustering encapsulates the diversity of the population, whereas other attributes show the efficiency of the network. The experiments were performed for one selected DE/PSO strategy and one simple test function.

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