Abstract
Swarm intelligence is a kind of nature-inspired heuristic optimization technique. Different computation models usually take on relative uniform characteristic though they usually have distinct extrinsic forms. These intelligent algorithms are coupling with deterministic and stochastic, the contradiction between necessity and accidental unity, which promotes the "evolution" of the inheritance and the creative process: "stochastic" is adopted to give creative ability to the implemented "intelligent system", and a succession of "certainty" is acted to ensure the system is converging. In this paper, a computing framework of generalized swarm intelligence is proposed based on the unifying idea. The unified hiberarchy model and formalization description for swarm intelligence are represented. Several typical swarm intelligence algorithms, such as ant colony system (ACS), particle swarm optimization (PSO), estimation of distribution algorithms (EDA) and artificial immune algorithms (AIA) are addressed to validate the uniform idea of swarm intelligence, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal on Artificial Intelligence Tools
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
