Cultural-based multiobjective particle swarm optimization.

Multiobjective particle swarm optimization (MOPSO) algorithms have been widely used to solve multiobjective optimization problems. Most MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary process. In this paper, we introduce a cultural framework to adapt the personalized flight parameters of the mutated particles in a MOPSO, namely momentum and personal and global accelerations, for each individual particle based upon various types of knowledge in "belief space," specifically situational, normative, and topographical knowledge. A comprehensive comparison of the proposed algorithm with chosen state-of-the-art MOPSOs on benchmark test functions shows that the movement of the individual particle using the adapted parameters assists the MOPSO to perform efficiently and effectively in exploring solutions close to the true Pareto front while exploiting a local search to attain diverse solutions.

Epileptic EEG: a comprehensive study of nonlinear behavior.

In this study, the nonlinear properties of the electroencephalograph (EEG) signals are investigated by comparing two sets of EEG, one set for epileptic and another set for healthy brain activities. Adopting measures of nonlinear theory such as Lyapunov exponent, correlation dimension, Hurst exponent, fractal dimension, and Kolmogorov entropy, the chaotic behavior of these two sets is quantitatively computed. The statistics for the two groups of all measures demonstrate the differences between the normal healthy group and epileptic one. The statistical results along with phase-space diagram verify that brain under epileptic seizures possess limited trajectory in the state space than in healthy normal state, consequently behaves less chaotically compared to normal condition.