{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-bi-population-particle-swarm-optimizer-for","title":"A Bi-population Particle Swarm Optimizer for Learning Automata based Slow Intelligent System","arxiv_id":"1804.00768","date":"2018-04-03","proceeding":null,"authors":["Mohammad Hasanzadeh Mofrad","S. K. Chang"],"abstract":"Particle Swarm Optimization (PSO) is an Evolutionary Algorithm (EA) that\nutilizes a swarm of particles to solve an optimization problem. Slow\nIntelligence System (SIS) is a learning framework which slowly learns the\nsolution to a problem performing a series of operations. Moreover, Learning\nAutomata (LA) are minuscule but effective decision making entities which are\nbest suited to act as a controller component. In this paper, we combine two\nisolate populations of PSO to forge the Adaptive Intelligence Optimizer (AIO)\nwhich harnesses the advantages of a bi-population PSO to escape from the local\nminimum and avoid premature convergence. Furthermore, using the rich framework\nof SIS and the nifty control theory that LA derived from, we find the perfect\nmatching between SIS and LA where acting slowly is the pillar of both of them.\nBoth SIS and LA need time to converge to the optimal decision where this\nenables AIO to outperform standard PSO having an incomparable performance on\nevolutionary optimization benchmark functions.","url_abs":"http://arxiv.org/abs/1804.00768v1","url_pdf":"http://arxiv.org/pdf/1804.00768v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-bi-population-particle-swarm-optimizer-for","repo_url":"https://github.com/hmofrad/pso","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}