A rapidly converging artificial bee colony algorithm for portfolio optimization

Cura, Tunchan

doi:10.1016/j.knosys.2021.107505

Cited by 29 publications

(15 citation statements)

References 36 publications

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“…Cura proposed an Artificial Bee Colony (ABC) algorithm to address seven publicly available benchmark problems (five from OR-Library and one from XU030 and XU100 indices each) [50]. A cardinality constrained POP model was presented.…”

Section: E: Artificial Bee Colonymentioning

confidence: 99%

“…Figure 4 displays the solution methodologies for the POP arranged according to datasets. The state-of-the art meth- ods for the OR-Library dataset are; a hybrid of Variable Neighbourhood Search and Monte Carlo Search from [19], Modified Squirrel Search Algorithm from [43], and Artificial Bee Colony from [50]. Population-based metaheuristic is a favoured approach in addressing the OR-Library datasets.…”

Section: A Benchmark Datasetsmentioning

confidence: 99%

“…[20], [39], [40], [50] -Weekly price data of 5 stock markets (31 assets from Hang Seng (Hong Kong), 85 assets from DAX100 (Germany), 89 assets from FTSE100 (UK), 98 assets from S&P100 (USA), 225 assets from Nikkei225 (Japan)) from March 1992 to September 1997 [1].…”

Section: Datasetmentioning

confidence: 99%

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Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

et al. 2023

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This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Population-based metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area.

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Section: E: Artificial Bee Colonymentioning

confidence: 99%

Section: A Benchmark Datasetsmentioning

confidence: 99%

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Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

et al. 2023

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“…Finally, we compare TN-GEO with nine different leading SOTA optimizers covering a broad spectrum of algorithmic strategies for this specific combinatorial problem, based on and referred hereafter as: 1) GTS [26], the genetic algorithms, tabu search, and simulated annealing; 2) IPSO [27], an improved particle swarm optimization algorithm [27]; 3) IPSO-SA [28], a hybrid algorithm combining particle swarm optimization and simulated annealing; 4) PBILD [29], a population-based incremental learning and differential evolution algorithm; 5) GRASP [30], a greedy randomized adaptive solution procedure; 6) ABCFEIT [31], an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures; 7) HAAG [32], a hybrid algorithm integrating ant colony optimization, artificial bee colony and genetic algorithms; 8) VNSQP [33], a variable neighborhood search algorithm combined with quadratic programming; and, 9) RCABC [34], a rapidly converging artificial bee colony algorithm.…”

Section: Comparison With State-of-the-art Algorithmsmentioning

confidence: 99%

GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models

Alcazar¹,

Perdomo-Ortiz²,

Vakili³

et al. 2022

Preprint

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We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. Our Generator-Enhanced Optimization (GEO) strategy is flexible to adopt any generative model, from quantum to quantum-inspired or classical, such as Generative Adversarial Networks, Variational Autoencoders, or Quantum Circuit Born Machines, to name a few. Here, we focus on a quantum-inspired version of GEO relying on tensor-network Born machines and referred to hereafter as TN-GEO. We present two prominent strategies for using TN-GEO. The first uses data points previously evaluated by any quantum or classical optimizer, and we show how TN-GEO improves the performance of the classical solver as a standalone strategy in hard-to-solve instances. The second strategy uses TN-GEO as a standalone solver, i.e., when no previous observations are available. Here, we show its superior performance when the goal is to find the best minimum given a fixed budget for the number of function calls. This might be ideal in situations where the cost function evaluation can be very expensive. To illustrate our results, we run these benchmarks in the context of the portfolio optimization problem by constructing instances from the S&P 500 and several other financial stock indexes. We show that TN-GEO can propose unseen candidates with lower cost function values than the candidates seen by classical solvers. This is the first demonstration of the generalization capabilities of quantum-inspired generative models that provide real value in the context of an industrial application. We also comprehensively compare state-of-the-art algorithms in a generalized version of the portfolio optimization problem. The results show that TN-GEO is among the best compared to these state-of-the-art algorithms; a re- markable outcome given the solvers used in the comparison have been fine-tuned for decades in this real-world industrial application. We see this as an important step toward a practical advantage with quantum-inspired models and, subsequently, with quantum generative models.

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“…Meanwhile, Meng et al [22] revisited the bi-criteria portfolio optimization model with permissible short selling. Next, Cura [23] developed a heuristic approach to the portfolio optimization problem by using the ABC technique.…”

Section: Introductionmentioning

confidence: 99%

An adaptive metaheuristic approach for risk-budgeted portfolio optimization

Gandikota

Hasan²,

Jaafar³

2023

IJ-AI

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<div align="left"><span lang="EN-US">An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential & arithmetic) together with the hall of fame via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study.</span></div>

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A rapidly converging artificial bee colony algorithm for portfolio optimization

Cited by 29 publications

References 36 publications

Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models

An adaptive metaheuristic approach for risk-budgeted portfolio optimization

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