Communication Diversity in Particle Swarm Optimizers

@inproceedings{Oliveira2016CommunicationDI,
  title={Communication Diversity in Particle Swarm Optimizers},
  author={Marcos A. C. Oliveira and Diego Pinheiro and Bruno Andrade and Carmelo Jos{\'e} Albanez Bastos Filho and Ronaldo Parente de Menezes},
  booktitle={ANTS Conference},
  year={2016}
}
Since they were introduced, Particle Swarm Optimizers have suffered from early stagnation due to premature convergence. Assessing swarm spatial diversity might help to mitigate early stagnation but swarm spatial diversity itself emerges from the main property that essentially drives swarm optimizers towards convergence and distinctively distinguishes PSO from other optimization techniques: the social interaction between the particles. The swarm influence graph captures the structure of particle… 
View via Publisher
marcosoliveira.info
15 Citations
Background Citations
5
Methods Citations
1

Figures from this paper

15 Citations

Citation Type

Citation Type

Uncovering the social interaction network in swarm intelligence algorithms
TLDR
This work investigates the structure of social interaction in four swarm-based algorithms, showing that the swarm interaction network approach enables researchers to study distinct algorithms from a common viewpoint, and provides an in-depth case study of the Particle Swarm Optimization.
Better exploration-exploitation pace, better swarm: Examining the social interactions
TLDR
This work examines the impact of the exploration-exploitation balance on the swarm performance by controlling the pace at which the swarm goes from exploration to exploitation and revealed that different problems demand distinct paces.
Characterizing the Social Interactions in the Artificial Bee Colony Algorithm
TLDR
This work proposes a definition of the interaction network for the Artificial Bee Colony (ABC) algorithm, and uncovered the different patterns of social interactions that emerge from each type of bee, revealing the importance of the bees variations throughout the iterations of the algorithm.
An approach to assess swarm intelligence algorithms based on complex networks
TLDR
The concept of interaction networks is employed to capture the interaction patterns that take place in algorithms during the optimisation process, and the analyses of these networks reveal aspects of the algorithm such as the tendency to achieve premature convergence, population diversity, and stability.
Uncovering the Social Interaction in Swarm Intelligence with Network Science
TLDR
A network-based framework---the interaction network---is introduced to examine computational swarm-based systems via the optics of the social dynamics of such interaction network; a clear example of network science being applied to bring further clarity to a complicated field within artificial intelligence.
Unveiling Swarm Intelligence with Network Science - the Metaphor Explained
TLDR
The interaction network enables a better understanding of the plethora of approaches currently available by looking at them from a general perspective focusing on the structure of the social interactions by introducing a network-based framework to examine computational swarm-based systems via the optics of social dynamics.
Modelling the Social Interactions in Ant Colony Optimization
TLDR
It is argued that the interaction-network approach may lead to a unified way of seeing swarm systems and in the case of ACO, remove part of the reliance on experts for parameter choice.
Assessing Ant Colony Optimization Using Adapted Networks Science Metrics
TLDR
It is demonstrated that two network science metrics, the Clustering coefficient and the Assortativity, can be adapted and used to assess the pheromone graph and extract information to identify the convergence state of the ACO.
A mayfly optimization algorithm
Particle swarm optimisation in nonlinear model predictive control; comprehensive simulation study for two selected problems
Direct model predictive control applications for nonlinear systems can result in non-convex minimisation problems, which is additionally complicated if systems are non-minimum phase. In this paper,…
...
1
2
...

References

SHOWING 1-10 OF 18 REFERENCES
Towards a network-based approach to analyze particle swarm optimizers
TLDR
This work proposes that the analysis of the particles interactions can be used to assess the swarm search mode, without the need for any particles properties, and defines the weighted swarm influence graph Ittw that keeps track of the interactions from the last tw iterations before a given iteration t.
Using network science to assess particle swarm optimizers
TLDR
The concept of the swarm influence graph is introduced to capture the information exchange between the particles in a given iteration during the execution of the algorithm to define a fingerprint for the swarm search behavior.
Assessing Particle Swarm Optimizers Using Network Science Metrics
TLDR
The definition of the influence graph of the swarm is proposed, specifically the R-value, the number of zero eigenvalues of the Laplacian Matrix, and the Spectral Density, in order to assess the behavior of the particles during the search and diagnose stagnation processes.
Measuring exploration/exploitation in particle swarms using swarm diversity
  • O. Olorunda, A. Engelbrecht
  • Computer Science, Geology
    2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
  • 2008
TLDR
This paper takes a look at some of the different definitions of swarm diversity with the intention of determining their usefulness in quantifying swarm exploration/exploitation to lay the foundations for the development of a suitable means to quantify the rate of change of diversity.
The fully informed particle swarm: simpler, maybe better
TLDR
The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms, but each individual is not simply influenced by the best performer among his neighbors.
Monitoring of particle swarm optimization
TLDR
A diversity measurement called cognitive diversity is discussed and defined, which can reveal clustering information about where the current population of particles intends to move towards and which stage it moves towards.
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
TLDR
This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Adaptive Clan Particle Swarm Optimization
TLDR
A deep analysis on the adaptation process for one multimodal function and the results revealed that the proposed incorporation of auto-adaptation capability in a cooperative Particle Swarm Optimization algorithm achieved better performance than other approaches, specially in tough multi-modal problems.
Diversity control in particle swarm optimization
TLDR
Several methods for diversity control of particle swarm optimization are tested on benchmark functions, and the method based on current position and average of current velocities has the best performance.
Adaptive Particle Swarm Optimization
TLDR
An adaptive particle swarm optimization with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach is proposed, resulting in substantially improved quality of global solutions.
...
1
2
...