This paper proposes a TSK-type Neuro-Fuzzy system tuned with a novel learning algorithm. The proposed algorithm used an improved version of the standard Particle Swarm Optimization algorithm, it employs several sub-swarms to explore the search space more efficiently. Each particle in a sub-swarm correct her position based on the best other positions, and the useful information is exchanged amon...

Swarms are a metaphor for complex dynamic systems. In swarms, large numbers of individuals locally interact and form non-linear, dynamic interaction networks. Ants, wasps and termites, for instance, are natural swarms whose individual and group behaviors have been evolving over millions of years. In their intricate nest constructions, the emergent effectiveness of their behaviors becomes appare...

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales’ behavior of communicating with each other via ultras...

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

In this paper, a new approach to Particle Swarm Optimization (PSO) using digital pheromones to coordinate swarms within an n-dimensional design space is presented. In a basic PSO, an initial randomly generated population swarm propagates towards the global optimum over a series of iterations. The direction of the swarm movement in the design space is based on an individual particle’s best posit...

Upon mapping swarm robots search to particle swarm optimization (PSO), we extend PSO algorithm to model and control swarm robots for target search at an abstract level. At first, we analytically compare the characteristics of different versions of PSO to facilitate design of robots control algorithm by tailoring and transferring parallel asynchronous property of PSO into case of swarm search. N...

Satellite swarms, consisting of a large number of identical, miniaturized and simple satellites, are claimed to provide an implementation for specific space missions which require high reliability. However, a consistent model of how reliability and availability on mission level is linked to costand time-effective design of the individual swarm satellites has not yet been done. We have establish...

The performance of Particle Swarm Optimization is greatly affected by the size and sociometry of the swarm. This research proposes a dynamic sociometry, which is shown to be more effective on some problems than the standard star and ring sociometries. The performance of various combinations of swarm size and sociometry on six different test functions is qualitatively analyzed.

This paper describes SWARMUSIC, an interactive music improviser. A particle swarm algorithm is used to generate musical material by a mapping of particle positions onto events in MIDI space. Interaction with an external musical source arises through the attraction of the particle swarm to a target. SWARMUSIC is the first application of swarm intelligence to music.

Swarm intelligence describes the ability of groups of social animals and insects to exhibit highly organized and complex problem-solving behaviors that allow the group as a whole to accomplish tasks which are beyond the capabilities of any one of the constituent individuals. This natural phenomenon is the inspiration for swarm intelligence systems, a class of algorithms that utilizes the emerge...