Comparing algorithms, representations and operators for the multi-objective knapsack problem

This paper compares the performance of three evolutionary multi-objective algorithms on the multi-objective knapsack problem. The three algorithms are SPEA2 (strength Pareto evolutionary algorithm, version 2), MOGLS (multi-objective genetic local search) and SEAMO2 (simple evolutionary algorithm for...

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Bibliographic Details
Published in:2005 IEEE Congress on Evolutionary Computation Vol. 2; pp. 1268 - 1275 Vol. 2
Main Authors: Colombo, G., Mumford, C.L.
Format: Conference Proceeding
Language:English
Published: IEEE 2005
Subjects:
Biological cells
Computer science
Decoding
Evolutionary computation
Genetics
Pareto optimization
Standards development
Testing
ISBN:0780393635, 9780780393639
ISSN:1089-778X
Online Access:Get full text
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Summary:This paper compares the performance of three evolutionary multi-objective algorithms on the multi-objective knapsack problem. The three algorithms are SPEA2 (strength Pareto evolutionary algorithm, version 2), MOGLS (multi-objective genetic local search) and SEAMO2 (simple evolutionary algorithm for multi-objective optimization, version 2). For each algorithm, we try two representations: bit-string and order-based. Our results suggest that a bit-string representation works best for MOGLS, but that SPEA2 and SEAMO2 perform better with an order-based approach. Although MOGLS outperforms the other algorithms in terms of solution quality, SEAMO2 runs much faster than its competitors and produces results of a similar standard to SPEA2.
ISBN:0780393635
9780780393639
ISSN:1089-778X
DOI:10.1109/CEC.2005.1554836