CULTURAL SWARMS - Knowledge-driven Framework for Solving Nonlinearly Constrained Global Optimization Problems

Mostafa Z. Ali; Yaser Khamayseh; Robert G. Reynolds

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Paper

CULTURAL SWARMS - Knowledge-driven Framework for Solving Nonlinearly Constrained Global Optimization Problems

Topics: Co-Evolution and Collective Behavior; Evolution Strategies; Machine Learning; Society and Cultural Aspects of Evolution; Swarm Intelligence

In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, 103-110, 2009 , Funchal - Madeira, Portugal

Authors: Mostafa Z. Ali ¹ ; Yaser Khamayseh ¹ and Robert G. Reynolds ²

Affiliations: ¹ Jordan University of Science & Technology, Jordan ; ² Wayne State University, United States

Keyword(s): Evolutionary computation, Nonlinearly constrained global optimization problem, Cultural swarms, Social interaction, Knowledge source interaction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Co-Evolution and Collective Behavior ; Computational Intelligence ; Evolution Strategies ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Society and Cultural Aspects of Evolution ; Soft Computing ; Swarm Intelligence ; Symbolic Systems

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 t he 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. (More)

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Paper citation in several formats:

Z. Ali, M.; Khamayseh, Y. and G. Reynolds, R. (2009). CULTURAL SWARMS - Knowledge-driven Framework for Solving Nonlinearly Constrained Global Optimization Problems. In Proceedings of the International Joint Conference on Computational Intelligence (IJCCI 2009) - ICEC; ISBN 978-989-674-014-6; ISSN 2184-3236, SciTePress, pages 103-110. DOI: 10.5220/0002282301030110

@conference{icec09,
author={Mostafa {Z. Ali}. and Yaser Khamayseh. and Robert {G. Reynolds}.},
title={CULTURAL SWARMS - Knowledge-driven Framework for Solving Nonlinearly Constrained Global Optimization Problems},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence (IJCCI 2009) - ICEC},
year={2009},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002282301030110},
isbn={978-989-674-014-6},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the International Joint Conference on Computational Intelligence (IJCCI 2009) - ICEC
TI - CULTURAL SWARMS - Knowledge-driven Framework for Solving Nonlinearly Constrained Global Optimization Problems
SN - 978-989-674-014-6
IS - 2184-3236
AU - Z. Ali, M.
AU - Khamayseh, Y.
AU - G. Reynolds, R.
PY - 2009
SP - 103
EP - 110
DO - 10.5220/0002282301030110
PB - SciTePress