Abstract
The approach of probability collectives (PC) in the collective intelligence (COIN) framework is one of the emerging artificial intelligence tools dealing with the complex problems in a distributed way. It decomposes the entire system into subsystems and treats them as a multi-agent system (MAS). These agents iteratively select their strategies to optimise their local goals which make the system to achieve the global optimum. This paper demonstrates the ability of PC solving discrete as well as mixed variable problems. The approach has produced competent and sufficiently robust results with comparatively higher computational cost. The associated strengths, weaknesses and possible real world extensions are also discussed.
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