Capturing Inner Dynamics of Firefly Algorithm in Complex Network—Initial Study

Janoštík, Jakub; Pluháček, Michal; Šenkeřík, Roman; Zelinka, Ivan; Špaček, František

doi:10.1007/978-3-319-29504-6_53

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…In the case of FA, the connection is created, when firefly flies towards another and improves own brightness. This leads to network presented in Figure 7 (Janostik et al 2016b). Duplicate connections were omitted.…”

Section: Visualizations For Famentioning

confidence: 99%

A Review On The Simulation of Social Networks Inside Heuristic Algorithms

Šenkeřík¹,

Pluháček²,

Viktorin³

et al. 2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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This paper represents a comprehensive review of selected methods for visualization of the population dynamics of the swarm and evolutionary algorithms in the form of networks. The whole idea is based on the obvious similarity between interactions between individuals in a swarm and evolutionary algorithms and for example, users of social networks, society, etc. The analogy between individuals from the population used in an arbitrary evolutionary or swarm-based algorithm and vertices (nodes) of a network is discussed here, as well as between edges in a network and communication between individuals in a population. Simple experiments with four well-known heuristic algorithms are described here, giving an insight into different approaches to the building of the network during metaheuristic run.

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Section: Visualizations For Famentioning

confidence: 99%

A Review On The Simulation of Social Networks Inside Heuristic Algorithms

Šenkeřík¹,

Pluháček²,

Viktorin³

et al. 2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

Self Cite

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“…Recently, Janostik et al [35] introduced a graph representation of the swarm evolution in the Particle Swarm Optimization (PSO) and the diversity measure based on this graph representation. In [36], the networks are used to capture inner dynamics of Firey Algorithm and in [37], the networks are used to capture the inner dynamics of the PSO. As mentioned before, the goal of this work is to analyze the dynamics of the dierential evolution algorithms using networks.…”

Section: Evolutionary Algorithms Dynamics Represented By Complex Netwmentioning

confidence: 99%

Differential Evolution Dynamics Analysis Using Aggregated Networks

Skanderová

Zelinka

2017

J. ADV. ENG. COMPUT.

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In this paper, the dynamics of the selected variants of the differential evolution is modelled by aggregated network capturing the relationships between individuals established during the population evolution. The motivation of this research is to better understand the relationships between individuals of the selected variants of the differential evolution. Thanks to the analysis of the aggregated networks, the advantages as well as bottlenecks of the selected algorithms can be specified more precisely and the results of the analysis can be used to develop novel algorithms. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Till now, all experiments focused on networks, where edges represented interactions between individuals in the population. E.g., crossing between individuals in the population in the DE [12,11], interactions between the leader and all other individuals in the SOMA [19,15], or interactions between fireflies in the firefly algorithm [7].…”

Section: Figure 1: Motivationmentioning

confidence: 99%

“…It means that if we use logarithmic scales for the degree distribution, the plot will create the line. Mathematically, the degree meets the (7). k represents the degree, p k represents the probability that a randomly selected vertex has a degree equals to k and α and c are the constants.…”

Section: Cn Analysis -Degree Distributionmentioning

confidence: 99%

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SOMA Network Model Based on Native Visibility Graph

Tomaszek¹,

Zelinka²

2017

mendel

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In this article, we want to propose a new model of the network for analyzing the evolution algorithms.We focus on the graph called native visibility graph. We show how we can get a time series from the run ofthe self-organizing migrating algorithm and how we can convert these series into a network. At the end of thearticle, we focus on some basic network properties and we propose how can we use these properties for laterinvestigation. All experiments run on well-known CEC 2016 benchmarks.

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Capturing Inner Dynamics of Firefly Algorithm in Complex Network—Initial Study

Cited by 13 publications

References 6 publications

A Review On The Simulation of Social Networks Inside Heuristic Algorithms

A Review On The Simulation of Social Networks Inside Heuristic Algorithms

Differential Evolution Dynamics Analysis Using Aggregated Networks

SOMA Network Model Based on Native Visibility Graph

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