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Probability Collectives [electronic resource] : A Distributed Multi-agent System Approach for Optimization / by Anand Jayant Kulkarni, Kang Tai, Ajith Abraham.

By: Kulkarni, Anand Jayant [author.].
Contributor(s): Tai, Kang [author.] | Abraham, Ajith [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 86Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: IX, 157 p. 68 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319160009.Subject(s): Engineering | Artificial intelligence | Statistical physics | Dynamical systems | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Statistical Physics, Dynamical Systems and ComplexityAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction to Optimization -- Probability Collectives: A Distributed Optimization Approach -- Constrained Probability Collectives: A Heuristic Approach -- Constrained Probability Collectives with a Penalty Function Approach -- Constrained Probability Collectives With Feasibility-Based Rule I -- Probability Collectives for Discrete and Mixed Variable Problems -- Probability Collectives with Feasibility-Based Rule II.
In: Springer eBooksSummary: This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
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Introduction to Optimization -- Probability Collectives: A Distributed Optimization Approach -- Constrained Probability Collectives: A Heuristic Approach -- Constrained Probability Collectives with a Penalty Function Approach -- Constrained Probability Collectives With Feasibility-Based Rule I -- Probability Collectives for Discrete and Mixed Variable Problems -- Probability Collectives with Feasibility-Based Rule II.

This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.

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