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Mathematics
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Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications
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Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 18651

Special Issue Editors

Prof. Dr. Siddhartha Bhattacharya

E-Mail Website
Guest Editor
Rajnagar Mahavidyalaya, Birbhum 731130, West Bengal, India
Interests: computational intelligence; quantum computing; pattern recognition
Dr. Mohamed Abd Elaziz

E-Mail Website
Guest Editor
Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt
Interests: biomedical informatics; artificial intelligence; deep learning; machine learning; 6G networks; signal processing; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of hybrid metaheuristics has flourished over the years due to the inherent vision of hybridization to combine different metaheuristics such that each of the combinations supplements the other in order to achieve the desired performance. Typical examples include fuzzy-evolutionary, neuro-evolutionary, neuro-fuzzy evolutionary, and rough-evolutionary approaches, to name a few. Quantum Metaheuristics enhance the real-time performance of the hybrid metaheuristics by resorting to the features of quantum mechanics.

Recently, portfolio optimization has attracted attention for helping investors to balance the risks and returns. An optimized portfolio enables proactive management of application lifecycles, changes, and standards. Therefore, portfolio management has thus become very important for reaching a resolution in high-risk investment opportunities and addressing the risk–reward tradeoff by maximizing returns and minimizing risks within a given investment period for a variety of assets. Apart from financial transactions, it can be extended to other areas including the healthcare sector, economic load dispatch, and load balancing in robotic vision, to name a few. Since portfolio optimization manifests real-world constraints, the problem becomes difficult to address via traditional optimization methods. In contrast, several hybrid metaheuristic approaches have been developed of late, to tackle portfolio optimization while avoiding the limitations of traditional methods.

Welcome topics include (but are not limited to) the following:

  • Hybrid metaheuristics;
  • Quantum-inspired hybrid metaheuristics;
  • Single-objective and multi-objective incarnations;
  • Multi-valued quantum logic-based quantum metaheuristics;
  • Robotic load balancing;
  • Gene portfolio analysis;
  • Financial portfolio management;
  • Economic load dispatch;
  • Health care asset allocation;
  • Stocks and trading analysis;
  • Optimized IoT portfolio applications;
  • Blockchain-based IoT (B-IoT) transaction management.

Prof. Dr. Siddhartha Bhattacharya
Dr. Mohamed Abd Elaziz
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metaheuristics
  • portfolio optimization
  • hybrid metaheuristics
  • load dispatch
  • blockchain portfolios

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Published Papers (7 papers)

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Research

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19 pages, 6566 KiB  
Article
Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem
by Alaa A. K. Ismaeel, Essam H. Houssein, Doaa Sami Khafaga, Eman Abdullah Aldakheel, Ahmed S. AbdElrazek and Mokhtar Said
Mathematics 2023, 11(19), 4107; https://doi.org/10.3390/math11194107 - 28 Sep 2023
Cited by 21 | Viewed by 2138
Abstract
The osprey optimization algorithm (OOA) is a new metaheuristic motivated by the strategy of hunting fish in seas. In this study, the OOA is applied to solve one of the main items in a power system called economic load dispatch (ELD). The ELD [...] Read more.
The osprey optimization algorithm (OOA) is a new metaheuristic motivated by the strategy of hunting fish in seas. In this study, the OOA is applied to solve one of the main items in a power system called economic load dispatch (ELD). The ELD has two types. The first type takes into consideration the minimization of the cost of fuel consumption, this type is called ELD. The second type takes into consideration the cost of fuel consumption and the cost of emission, this type is called combined emission and economic dispatch (CEED). The performance of the OOA is compared against several techniques to evaluate its reliability. These methods include elephant herding optimization (EHO), the rime-ice algorithm (RIME), the tunicate swarm algorithm (TSA), and the slime mould algorithm (SMA) for the same case study. Also, the OOA is compared with other techniques in the literature, such as an artificial bee colony (ABO), the sine cosine algorithm (SCA), the moth search algorithm (MSA), the chimp optimization algorithm (ChOA), and monarch butterfly optimization (MBO). Power mismatch is the main item used in the evaluation of the OOA with all of these methods. There are six cases used in this work: 6 units for the ELD problem at three different loads, and 6 units for the CEED problem at three different loads. Evaluation of the techniques was performed for 30 various runs based on measuring the standard deviation, minimum fitness function, and maximum mean values. The superiority of the OOA is achieved according to the obtained results for the ELD and CEED compared to all competitor algorithms. Full article
(This article belongs to the Special Issue Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications)
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24 pages, 2009 KiB  
Article
Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods
by Rana Muhammad Adnan Ikram, Xinyi Cao, Kulwinder Singh Parmar, Ozgur Kisi, Shamsuddin Shahid and Mohammad Zounemat-Kermani
Mathematics 2023, 11(14), 3141; https://doi.org/10.3390/math11143141 - 16 Jul 2023
Cited by 2 | Viewed by 1268
Abstract
The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour [...] Read more.
The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error, determination coefficient and new graphical inspection methods (e.g., Taylor and violin charts). On the basis of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) statistics, it was observed that GPR provided the best accuracy in predicting short-term single-time-step and multi-time-step significant wave heights. On the basis of mean RMSE, GPR improved the accuracy of M5RT, MARS, PCR, RF and PLSR by 16.63, 8.03, 10.34, 3.25 and 7.78% (first station) and by 14.04, 8.35, 13.34, 3.87 and 8.30% (second station) for the test stage. Full article
(This article belongs to the Special Issue Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications)
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30 pages, 8001 KiB  
Article
An Archive-Guided Equilibrium Optimizer Based on Epsilon Dominance for Multi-Objective Optimization Problems
by Nour Elhouda Chalabi, Abdelouahab Attia, Abderraouf Bouziane, Mahmoud Hassaballah, Abed Alanazi and Adel Binbusayyis
Mathematics 2023, 11(12), 2680; https://doi.org/10.3390/math11122680 - 13 Jun 2023
Cited by 1 | Viewed by 1155
Abstract
In real-world applications, many problems involve two or more conflicting objectives that need to be optimized at the same time. These are called multi-objective optimization problems (MOPs). To solve these problems, we introduced a guided multi-objective equilibrium optimizer (GMOEO) algorithm based on the [...] Read more.
In real-world applications, many problems involve two or more conflicting objectives that need to be optimized at the same time. These are called multi-objective optimization problems (MOPs). To solve these problems, we introduced a guided multi-objective equilibrium optimizer (GMOEO) algorithm based on the equilibrium optimizer (EO), which was inspired by control–volume–mass balance models that use particles (solutions) and their respective concentrations (positions) as search agents in the search space. The GMOEO algorithm involves the integration of an external archive that acts as a guide and stores the optimal Pareto set during the exploration and exploitation of the search space. The key candidate population also acted as a guide, and Pareto dominance was employed to obtain the non-dominated solutions. The principal of ϵ-dominance was employed to update the archive solutions, such that they could then guide the particles to ensure better exploration and diversity during the optimization process. Furthermore, we utilized the fast non-dominated sort (FNS) and crowding distance methods for updating the position of the particles efficiently in order to guarantee fast convergence in the direction of the Pareto optimal set and to maintain diversity. The GMOEO algorithm obtained a set of solutions that achieved the best compromise among the competing objectives. GMOEO was tested and validated against various benchmarks, namely the ZDT and DTLZ test functions. Furthermore, a benchmarking study was conducted using cone-ϵ-dominance as an update strategy for the archive solutions. In addition, several well-known multi-objective algorithms, such as the multi-objective particle-swarm optimization (MOPSO) and the multi-objective grey-wolf optimization (MOGWO), were compared to the proposed algorithm. The experimental results proved definitively that the proposed GMOEO algorithm is a powerful tool for solving MOPs. Full article
(This article belongs to the Special Issue Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications)
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22 pages, 9199 KiB  
Article
Optimizing Kidney Stone Prediction through Urinary Analysis with Improved Binary Particle Swarm Optimization and eXtreme Gradient Boosting
by Abdullah Alqahtani, Shtwai Alsubai, Adel Binbusayyis, Mohemmed Sha, Abdu Gumaei and Yu-Dong Zhang
Mathematics 2023, 11(7), 1717; https://doi.org/10.3390/math11071717 - 3 Apr 2023
Cited by 5 | Viewed by 3355
Abstract
Globally, the incidence of kidney stones (urolithiasis) has increased over time. Without better treatment, stones in the kidneys could result in blockage of the ureters, repetitive infections in the urinary tract, painful urination, and permanent deterioration of the kidneys. Hence, detecting kidney stones [...] Read more.
Globally, the incidence of kidney stones (urolithiasis) has increased over time. Without better treatment, stones in the kidneys could result in blockage of the ureters, repetitive infections in the urinary tract, painful urination, and permanent deterioration of the kidneys. Hence, detecting kidney stones is crucial to improving an individual’s life. Concurrently, ML (Machine Learning) has gained extensive attention in this area due to its innate benefits in continuous enhancement, its ability to deal with multi-dimensional data, and its automated learning. Researchers have employed various ML-based approaches to better predict kidney stones. However, there is a scope for further enhancement regarding accuracy. Moreover, studies seem to be lacking in this area. This study proposes a smart toilet model in an IoT-fog (Internet of Things-fog) environment with suitable ML-based algorithms for kidney stone detection from real-time urinary data to rectify this issue. Significant features are selected using the proposed Improved MBPSO (Improved Modified Binary Particle Swarm Optimization) to attain better classification. In this case, sigmoid functions are used for better prediction with binary values. Finally, classification is performed using the proposed Improved Modified XGBoost (Modified eXtreme Gradient Boosting) to prognosticate kidney stones. In this case, the loss functions are updated to make the model learn effectively and classify accordingly. The overall proposed system is assessed by internal comparison with DT (Decision Tree) and NB (Naïve Bayes), which reveals the efficient performance of the proposed system in kidney stone prognostication. Full article
(This article belongs to the Special Issue Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications)
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25 pages, 5013 KiB  
Article
Modified Artificial Hummingbird Algorithm-Based Single-Sensor Global MPPT for Photovoltaic Systems
by Hesham Alhumade, Essam H. Houssein, Hegazy Rezk, Iqbal Ahmed Moujdin and Saad Al-Shahrani
Mathematics 2023, 11(4), 979; https://doi.org/10.3390/math11040979 - 14 Feb 2023
Cited by 8 | Viewed by 2055
Abstract
Recently, a swarm-based method called Artificial Hummingbird Algorithm (AHA) has been proposed for solving optimization problems. The AHA algorithm mimics the unique flight capabilities and intelligent foraging techniques of hummingbirds in their environment. In this paper, we propose a modified version of the [...] Read more.
Recently, a swarm-based method called Artificial Hummingbird Algorithm (AHA) has been proposed for solving optimization problems. The AHA algorithm mimics the unique flight capabilities and intelligent foraging techniques of hummingbirds in their environment. In this paper, we propose a modified version of the AHA combined with genetic operators called mAHA. The experimental results show that the proposed mAHA improved the convergence speed and achieved better effective search results. Consequently, the proposed mAHA was used for the first time to find the global maximum power point (MPP). Low efficiency is a drawback of photovoltaic (PV) systems that explicitly use shading. Normally, the PV characteristic curve has an MPP when irradiance is uniform. Therefore, this MPP can be easily achieved with conventional tracking systems. With shadows, however, the conditions are completely different, and the PV characteristic has multiple MPPs (i.e., some local MPPs and a single global MPP). Traditional MPP tracking approaches cannot distinguish between local MPPs and global MPPs, and thus simply get stuck at the local MPP. Consequently, an optimized MPPT with a metaheuristic algorithm is required to determine the global MPP. Most MPPT techniques require more than one sensor, e.g., voltage, current, irradiance, and temperature sensors. This increases the cost of the control system. In the current research, a simple global MPPT method with only one sensor is proposed for PV systems considering the shadow conditions. Two shadow scenarios are considered to evaluate the superiority of the proposed mAHA. The obtained results show the superiority of the proposed single sensor based MPPT method for PV systems. Full article
(This article belongs to the Special Issue Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications)
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27 pages, 2925 KiB  
Article
Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification
by Essam H. Houssein and Awny Sayed
Mathematics 2023, 11(3), 707; https://doi.org/10.3390/math11030707 - 30 Jan 2023
Cited by 51 | Viewed by 2982
Abstract
In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. [...] Read more.
In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO’s Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC’22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix. Full article
(This article belongs to the Special Issue Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications)
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Review

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44 pages, 870 KiB  
Review
A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering
by Alokananda Dey, Siddhartha Bhattacharyya, Sandip Dey, Debanjan Konar, Jan Platos, Vaclav Snasel, Leo Mrsic and Pankaj Pal
Mathematics 2023, 11(9), 2018; https://doi.org/10.3390/math11092018 - 24 Apr 2023
Cited by 6 | Viewed by 3509
Abstract
In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by [...] Read more.
In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algorithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits. Full article
(This article belongs to the Special Issue Hybrid Metaheuristic Algorithms for Portfolio Optimization and Its Applications)
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