Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor

Uncovering the social interaction network in swarm intelligence algorithms

This is the final version. Available from the publisher via the DOI in this record.Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction

Optimizing Associative Information Transfer within Content-addressable Memory

Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe

Relationship between earthquake fault triggering and societal behavior using ant colony optimization

In this analysis, we use the ant behaviour in simulating a framework for analysis of complex interplay amongst short time-scale deformation, long time- scale tectonics for positive stress coupling and slip interactions in earthquake genesis modeling. Using the proposed improved ant colony algorithm for global optimization the best solution ants within the search and the circulation of the optimal solution as the initial solution search, to expand its search, to avoid falling into local optimum  of  trigger zones analysis for earthquake occurrences. In order to validate the avalanche behaviour and corresponding nucleation we  best solution as the initial solution is adopted in order to widen searching scope to avoid getting into local optimum . In this proposed framework, an ant colony model is simulated to identify the physical framework of identifying trigger basins for the precursors to geodynamic model of propagation for precursory stress-strain signals. The disturbances at trigger basins cause the collapse of a subsystem leading to stress evolution and slip nucleation. Trigger basins help identify the zone of earthquake source nucleation as an index of ? and ? for strain analysis. The stress strain network can be interpreted by the increase in steady-state energy transmitted due to redistribution of stress accumulation into the earth tectonic framework. Sand pile behaviour model has been modeled through ant colony optimization for forecasting of likelihood time of triggering influences of lithosphere on the basis of critical zones of lithosphere where dump of elastic pressure is possible. The ant colony adaptive framework consisted of vertices representing the stress-strain component and edges, representing scored transformations for global coupling effects have been constructed for dynamic monitoring of stress and strain behaviour. Triggering basins serve as harbingers of large earthquake where stress-strain interactions have been analyzed by the quasi-static mechanics of seismic precursory stress-strain propagation in the crustal lithosphere. The study shows that dynamic variation of stress drop due to saved up pressure can be modeled by ant colony framework for steady state release due to trigger and global correlation framework. The simulation framework shows that with time, spatial triggering points can be negatively coupled and these interact with lesser impact, while positive coupling occurs only with more distant zones of stress generation for geodynamic frameworks, suggesting that the structural heterogeneities within the causative rocks associated with cracks and pores can dictate the pattern of stress – strain interactions and earthquake generating processes. Keywords: crack–porous, ant colony, geo-dynamical framework, stress-strain transmission, emergenc

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

Integration of evolutionary algorithm in an agent-oriented approach for an adaptive e-learning

This paper describes an agent- oriented approach that aims to create learning situations by solving problems. The proposed system is designed as a multi-agent that organizes interfaces, coordinators, sources of information and mobiles. The objective of this approach is to get learners to solve a problem that leads them to get engaged in several learning activities, chosen according to their level of knowledge and preferences in order to ensure adaptive learning and reduce the rate of learner abundance in an e-learning system. The search for learning activities procedure is based on evolutionary algorithms typically: genetic algorithm, to offer learners the optimal solution adapted to their profiles and ensuring a resolution of the proposed learning problem. In terms of results, we have adopted “immigration strategies” to improve the performance of the genetic algorithm. To show the effectiveness of the proposed approach we have made a comparative study with other artificial intelligence optimization methods. We conducted a real experiment with primary school learners in order to test the effectiveness of the proposed approach and to set up its functioning. The experiment results showed a high rate of success and engagement among the learners who followed the proposed adaptive learning scenario