This paper is based on swarm intelligence and chaotic dynamics for learning. We address this issue by considering the problem of web newspaper layout. This problem consists of optimizing the layout of a set of articles extracted from several web newspapers and sending it to the user as a result of a previous query. This layout should be organized in columns as that of real newspapers and should be adapted to the user's web browser configuration in real time. We propose a new approach to the problem based on an improved particle swarm optimization combined with chaos and chaotic annealing. The particle swarm optimization technique has ever since turned out to be a competitor in the field of numerical optimization. A particle swarm optimization consists of a number of individuals refining their knowledge of the given search space. Particle swarm optimizations are inspired by particles moving around in the search space. By introducing chaotic dynamics to simulated annealing, we propose an improved particle swarm optimization with chaotic annealing technique. The key idea of chaotic annealing is to take full advantages of ergodic property and stochastic property of chaotic system and replace the Gaussian distribution by chaotic sequences in simulated annealing. This paper proposes some basic concepts inspired by swarm intelligence, chaotic dynamics and simulated annealing for use in computational intelligence which promises greater efficiency and perhaps solvability of problems currently not amenable to a optimization approach.
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