This study proposes a modified version of cultural algorithms (CAs) which benefits from rule-based system for influence function. This rule-based system selects and applies the suitable knowledge source according to the distribution of the solutions. This is important to use appropriate influence function to apply to a specific individual, regarding to its role in the search process. This rule ...

The process of load balancing increases the performance of cloud based services. Cloud based service provide hardware, software and platform as service. For the balancing of public cloud used two types of approach one is traditional approach and other is dynamic approach. The traditional approach follows the concept of CPU scheduling technique such as round robin and first come first service. T...

This paper introduces collaborating robots which provide the possibility of enhanced task performance, high reliability and decreased. Collaborating-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration ...

Swarm intelligence via its infamous struggle to identify a suitable balance between exploration and exploitation phases, provides a valuable mean to approach artificial creativity. This work deploys two swarm intelligence algorithms, one simulating the behaviour of birds flocking and fish schooling (Particle Swarm Optimisation) and the other mimicking the behaviour of ants foraging (Stochastic ...

Previous work on swarms with variation in particle behaviours has focussed on discrete behaviours. In this paper we investigate a traitbased approach where particles in the swarm display degrees of different behaviour. Results show that trait-based diversity in the swarm leads to better performance than standard PSO and discrete behaviour variation.

Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is based on importance sampling. However, in the literature, the proper choice of the proposal distribution for importance sampling remains a tough task and has not been resolved yet. Inspired by the animal swarm intelligence ...

The particle swarm optimization algorithm (PSO) has two typical problems as in other adaptive evolutionary algorithms, which are based on swarm intelligence search. To deal with the problems of the slow convergence rate and the tendency to trap into premature, an improved particle swarm optimization with decline disturbance index (DDPSO) is presented in this paper. The index was added when the ...

Inspired by the migratory behavior in the nature, a novel particle swarm optimization algorithm based on particle migration (MPSO) is proposed in this work. In this new algorithm, the population is randomly partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization with time varying inertia weight and acceleration coefficients (LPSO-TVAC). At perio...