Constraint handling in probability collectives using a modified feasibility-based rule
by Anand J. Kulkarni; Neha S. Patankar; Kang Tai
International Journal of Computational Science and Engineering (IJCSE), Vol. 13, No. 4, 2016

Abstract: Almost all existing heuristic techniques are unconstrained optimisation methods and treat the system in centralised way. A distributed and decentralised optimisation technique in the framework of collective intelligence referred to as probability collectives (PCs) decomposes the entire system into subsystems and treats them as a multi-agent system. Similar to other contemporary heuristic techniques, its performance is significantly affected when constraints are involved. In order to handle constraints, a modified feasibility-based rule is incorporated into the PC algorithm. The approach is validated by solving a variety of constrained test problems. A tension/compression spring design problem, welded beam design problem and pressure vessel design problem are also solved. The approach is shown to be sufficiently robust and other strengths and weaknesses are also discussed. The solution to these problems proves that the constrained PC approach can be applied to a variety of practical/real world problems.

Online publication date: Tue, 08-Nov-2016

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