Improved Set-based Particle Swarm optimization for Portfolio optimization
| Autor: | Kyle Erwin, Andries P. Engelbrecht |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Linear programming Computer science Process (computing) Particle swarm optimization Approximation algorithm 02 engineering and technology Set (abstract data type) 020901 industrial engineering & automation Convergence (routing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quadratic programming Portfolio optimization |
| Zdroj: | SSCI |
| DOI: | 10.1109/ssci47803.2020.9308579 |
| Popis: | Recently, a set-based particle swarm optimization algorithm was proposed for portfolio optimization. The algorithm used a bi-stage search process that first selected assets and then determined the weights for said assets. Unlike other approaches before it, weight determination was accomplished by the use of a nature-inspired algorithm as opposed to quadratic programming. The algorithm was shown to be capable of obtaining goodquality solutions while being relatively fast. However, there were several aspects of the algorithm that could be improved upon, namely, convergence, efficiency, and in some cases, the ability to approximate the known Pareto optimal front. This paper suggests and investigates two modified versions of the set-based algorithm that are shown to yield substantial gains in performance. The latter, referred to as improved set-based particle swarm optimization, was able to find portfolios that are more, or as profitable as those obtained by the original approach, but faster and with lower levels of risk. |
| Databáze: | OpenAIRE |
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