Emergence: from chaos to order
Emergence: from chaos to order
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Particle Swarm Optimisation for Protein Motif Discovery
Genetic Programming and Evolvable Machines
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Mathematics and Computers in Simulation
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network
Applied Soft Computing
Pattern Recognition Letters
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A multi-swarm approach for neighbor selection in peer-to-peer networks
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Optimal matching by the transiently chaotic neural network
Applied Soft Computing
A novel particle swarm optimizer hybridized with extremal optimization
Applied Soft Computing
A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems
Fundamenta Informaticae - Swarm Intelligence
Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm
Future Generation Computer Systems
Information Sciences: an International Journal
Stability analysis of swarms with interaction time delays
Information Sciences: an International Journal
A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems
Fundamenta Informaticae - Swarm Intelligence
Swarm intelligence approaches to estimate electricity energy demand in Turkey
Knowledge-Based Systems
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Swarm intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. The intelligence emerges from a chaotic balance between individuality and sociality. The chaotic balances are a characteristic feature of the complex system. This paper investigates the chaotic dynamic characteristics in swarm intelligence. The swarm intelligent model namely the particle swarm (PS) is represented as an iterated function system (IFS). The dynamic trajectory of the particle is sensitive on the parameter values of IFS. The Lyapunov exponent and the correlation dimension are calculated and analyzed numerically for the dynamic system. Our research results illustrate that the performance of the swarm intelligent model depends on the sign of the maximum Lyapunov exponent. The particle swarm with a high maximum Lyapunov exponent usually achieves better performance, especially for multi-modal functions.