Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74398
Title: Multi-objective comprehensive learning bacterial foraging optimization for portfolio problem
Authors: Niu, B
Yi, W
Tan, L
Liu, J
Li, Y
Wang, H 
Keywords: Bacterial foraging optimization
Comprehensive learning strategy
Multi-objective problem
Pareto solutions
Portfolio optimization
Issue Date: 2017
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10386, p. 69-76 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Multi-objective portfolio optimization (PO) problem is always converted into a single objective problem by using the weighted method, which is sensitive to the pareto optimal front and requires that decision makers must have previous experience about the preference for weights. Based on multi-objective comprehensive learning bacterial foraging optimization (MOCLBFO), this paper proposes an algorithm which is specially designed for multi-objective PO problem. The corresponding coding strategy which considers each particle as a feasible solution is also given. In order to test the validity of the algorithm, multi-objective comprehensive learning particle swarm optimization (MOCLPSO) is chosen as the competing algorithm. Comparative experimental tests on ten assets PO problem demonstrate that MOCLBFO is able to find a more well-distributed Pareto set.
Description: 8th International Conference on Swarm Intelligence, ICSI 2017, 27 July 2017 - 1 August 2017
URI: http://hdl.handle.net/10397/74398
ISBN: 9783319618326
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-61833-3_8
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