Nature-inspired informatics for intelligent applications and by Raymond Chiong

By Raymond Chiong

Lately, nature has influenced many winning suggestions, algorithms, and computational functions permitting conventionally tricky difficulties to be solved via novel computing platforms.

Nature-Inspired Informatics for clever purposes and data Discovery: Implications in enterprise, technology, and Engineering offers the most recent findings in nature-inspired algorithms and their functions for breakthroughs in a variety of disciplinary fields. This defining reference assortment comprises chapters written by means of major researchers and recognized academicians in the box, providing readers a helpful and enriched accumulation of information.

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0 excluded and 1 included. The fitness value of the newly generated xi is calculated and if it outperforms the previous local best value the value of xi−lb is updated. Similarly, if the newly generated solution outperforms the global best solution, a replacement occurs. At the end of the optimisation process, the final global best detected is the estimate of the global optimum returned by the particle swarm algorithm. It is important to remark that in particle swarm optimisation, there is a population of particles which has the role of exploring the decision space and a population of local best solutions (the global best is the local best with the highest performance) to keep track of the successful movements.

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Technical Report C3P-790). Pasadena, CA: California Institute of Technology. , & Simutis, R. (2005). Adapting particle swarm optimization to stock markets. In Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (pp. 520-525). Washington, DC: IEEE Computer Society. , & Dounias, G. (2008). Nature inspired intelligence in medicine: Ant colony optimization for Pap-smear diagnosis. International Journal of Artificial Intelligence Tools, 17(2), 279–301. , Cascella, G.

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