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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with two other existing and well-known multiobjective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulty. In particular, the proposed algorithm is found to be significantly superior for many objective test problems (e.g., 4, 5, 10, and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm, where the performance of the former is found to be generally superior to that of the latter.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2007.900837