A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA

This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solutio...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 12; no. 3; pp. 269 - 283
Main Authors:	Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.06.2008 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithm design and analysis Algorithms Amount of domination Applied sciences archive Archives Artificial intelligence clustering Clustering algorithms Computer science; control theory; systems Computer simulation Costs Evolutionary algorithms Evolutionary computation Exact sciences and technology Heuristic Land surface temperature Learning and adaptive systems Machine intelligence Mathematical models multiobjective optimization (MOO) Optimization Pareto optimality Pareto-optimal (PO) Performance evaluation Simulated annealing simulated annealing (SA) Studies Testing archive clustering Amount of domination multiobjective optimization (MOO) simulated annealing (SA) Pareto-optimal (PO) Cluster analysis Acceptance Archive Probabilistic approach Pareto optimum Hierarchical classification Evolutionary algorithm Multiobjective programming Optimization Algorithm complexity Simulated annealing
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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