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… 
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15 Citations
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Methods Citations
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15 Citations

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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,…
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