Meta-heuristics for Portfolio Optimization: Part II-Empirical Analysis

被引：0

作者：

Erwin, Kyle ^{[1
]}

Engelbrecht, Andries ^{[1
,2
,3
]}

机构：

[1] Stellenbosh Univ, Comp Sci Div, Stellenbosch, South Africa

[2] Stellenbosh Univ, Dept Ind Engn, Stellenbosch, South Africa

[3] Gulf Univ Sci & Technol, Ctr Appl Math & Bioinformat, Mubarak Al Abdullah, Kuwait

来源：

ADVANCES IN SWARM INTELLIGENCE, ICSI 2023, PT II | 2023年 / 13969卷

关键词：

Set-based Particle Swarm Optimization; Particle Swarm Optimization; Artificial Bee Colony; Firefly Algorithm; Genetic Algorithm; Portfolio Optimization;

D O I：

10.1007/978-3-031-36625-3_36

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A companion paper identified five meta-heuristic approaches for the unconstrained portfolio optimization problem. Four of which, artificial bee colony (ABC), firefly algorithm (FA), a genetic algorithm (GA) and particle swarm optimization (PSO), are very popular approaches to the problem. The fifth meta-heuristic identified, set-based particle swarm optimization (SBPSO), is a new set-based approach that redefines the portfolio optimization problem as a combinatorial optimization problem. This paper investigates the performance of SBPSO against the aforementioned popular approaches to portfolio optimization. It is shown that SBPSO is a highly competitive approach to portfolio optimization. Furthermore, SBPSO scaled to larger portfolio problems without a reduction in performance, while being significantly faster than the other algorithms.

引用

页码：453 / 464

页数：12

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