Paper Info
Title
Constraint handling in probability collectives using a modified feasibility-based rule
Abstract
AbstractAlmost 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.
Year
DOI
Venue
2016
10.1504/IJCSE.2016.080208
Periodicals
Keywords
Field
DocType
probability collectives, multi-agent system, MAS, collective intelligence, COIN, feasibility-based rule, constrained test problems.
Heuristic,Agent based systems,Collective intelligence,Computer science,Multi-agent system,Artificial intelligence,Strengths and weaknesses,Machine learning,Distributed computing
Journal
Volume
Issue
ISSN
13
4
1742-7185
Citations 
PageRank 
References 
0
0.34
25
Authors
3
Name
Order
Citations
PageRank
Anand J. Kulkarni1887.80
Neha S. Patankar231.08
K. Tai317722.25