Paper Info
Title
Cultural Swarms Knowledge-Driven Framework For Solving Nonlinearly Constrained Global Optimization Problems
Abstract
In this paper we investigate how diverse knowledge sources interact to direct individuals in a swarm population influenced by a social fabric approach to efficiently solve nonlinearly constrained global minimization problems. We identify how knowledge sources used by Cultural Algorithms are combined to direct the decisions of the individual agents in solving optimization problems using an influence function family based upon a Social Fabric metaphor. The interaction of these knowledge sources with the population swarms produced emergent phases of problem solving. This reflected an algorithmic process that emerged from the interaction of the knowledge sources under the influence of a social fabric using different configurations. This suggests that the social interaction of individuals coupled with their interaction with a culture within which they are embedded provides a powerful vehicle for the solution of nonlinearly constrained optimization problems. The algorithm can escape from the previously converged local minimizers, and can converge to an approximate global minimizer of the problem asymptotically. Numerical experiments show that it is better than many other well-known recent methods for constrained global optimization.
Year
Venue
Keywords
2009
IJCCI 2009: PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL INTELLIGENCE
Evolutionary computation, Nonlinearly constrained global optimization problem, Cultural swarms, Social interaction, Knowledge source interaction
Field
DocType
Citations 
Mathematical optimization,Computer science,Constrained optimization,Global optimization problem
Conference
0
PageRank 
References 
Authors
0.34
1
3
Name
Order
Citations
PageRank
Mostafa Z. Ali125219.32
Yaser Khamayseh2589.11
Robert G. Reynolds3610188.20