Title: Probability collectives for solving discrete and mixed variable problems

Authors: Anand J. Kulkarni; Ishaan R. Kale; K. Tai

Addresses: Optimization and Agent Technology (OAT) Research Lab, Maharashtra Institute of Technology, 124 Paud Road, Pune 411038, India; School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore ' Optimization and Agent Technology (OAT) Research Lab, Maharashtra Institute of Technology, 124 Paud Road, Pune 411038, India ' School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore

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.

Keywords: probability collectives; collective intelligence; COIN; multi-agent systems; MAS; agent-based systems; artificial intelligence; discrete problems; mixed variable problems.

DOI: 10.1504/IJCAET.2016.079387

International Journal of Computer Aided Engineering and Technology, 2016 Vol.8 No.4, pp.325 - 361

Received: 27 Jan 2014
Accepted: 18 Mar 2014
Published online: 28 Sep 2016 *

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