Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Дек. 18, 2023

Abstract The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. Cleveland Heart disease dataset with sixteen techniques three categories filter, wrapper, and evolutionary were used. Then seven Bayes net, Naïve (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, Random Forest applied to identify best models prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, PRC measured compare methods' effect on prediction algorithms. results demonstrate that resulted significant improvements performance some (e.g., j48), whereas it led a decrease other (e.g. MLP, RF). SVM-based filtering have best-fit accuracy 85.5. In fact, best-case scenario, result + 2.3 accuracy. SVM-CFS/information gain/Symmetrical uncertainty highest improvement this index. filter number features selected outperformed terms models' ACC, F-measures. However, wrapper-based improved from sensitivity specificity points view.

Язык: Английский

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Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 20, 2024

Язык: Английский

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Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Heliyon, Год журнала: 2024, Номер 10(5), С. e26665 - e26665

Опубликована: Март 1, 2024

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by growth and proliferation patterns of liver tumors. MOLCA emulates evolutionary tendencies tumors, leveraging their expansion dynamics as model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with Random Opposition-Based Learning (ROBL) strategy, optimizing both local global search capabilities. Further enhancement is achieved through integration elitist non-dominated sorting (NDS), information feedback mechanism (IFM) Crowding Distance (CD) selection method, which collectively aim to efficiently identify Pareto optimal front. performance rigorously assessed using comprehensive set standard test benchmarks, including ZDT, DTLZ various Constraint (CONSTR, TNK, SRN, BNH, OSY KITA) real-world design like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss Welded beam. Its efficacy benchmarked against prominent algorithms such grey wolf optimizer (NSGWO), multiobjective multi-verse (MOMVO), (NSGA-II), decomposition-based (MOEA/D) marine predator (MOMPA). Quantitative analysis conducted GD, IGD, SP, SD, HV RT metrics represent convergence distribution, while qualitative aspects are presented graphical representations fronts. source code available at: https://github.com/kanak02/MOLCA.

Язык: Английский

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Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm

International Journal of Computational Intelligence Systems, Год журнала: 2024, Номер 17(1)

Опубликована: Апрель 11, 2024

Abstract This research introduces a novel multi-objective adaptation of the Geometric Mean Optimizer (GMO), termed Multi-Objective (MOGMO). MOGMO melds traditional GMO with an elite non-dominated sorting approach, allowing it to pinpoint Pareto optimal solutions through offspring creation and selection. A Crowding Distance (CD) coupled Information Feedback Mechanism (IFM) selection strategy is employed maintain amplify convergence diversity potential solutions. efficacy capabilities are assessed using thirty notable case studies. encompasses nineteen benchmark problems without constraints, six constraints five engineering design challenges. Based on optimization results, proposed better 54.83% in terms GD, 64.51% IGD, 67.74% SP, 70.96% SD, HV 77.41% RT. Therefore, has for solving un-constraint, constraint real-world application. Statistical outcomes from compared those Equilibrium (MOEO), Decomposition-Based Symbiotic Organism Search (MOSOS/D), Non-dominated Sorting Genetic Algorithm (NSGA-II), Multi-Verse Optimization (MOMVO) Plasma Generation (MOPGO) algorithms, utilizing identical performance measures. comparison reveals that consistently exhibits robustness excels addressing array The source code available at https://github.com/kanak02/MOGMO .

Язык: Английский

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Integrated Multiobjective Energy Management for a Smart Microgrid Incorporating Electric Vehicle Charging Stations and Demand Response Programs Under Uncertainty

International Journal of Energy Research, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

This paper presents an innovative 24‐h scenario–based microgrid energy management system (MG‐EMS) designed to achieve cost reduction and emission under conditions of uncertainty. Furthermore, a multiobjective hybrid heuristic algorithm, named particle swarm optimization lightning search algorithm (hMOPSO‐LSA), is introduced tackle the MG‐EMS problem. combines LSA MOPSO algorithm. The MG investigation comprises photovoltaic (PV) wind turbine (WT) units, combined heat power (CHP) system, employs multicarrier storage technology, specifically, power‐to‐gas (P2G) technology electric vehicle (EV) parking lot (PL). Flexible loads are incorporated into enhance through participation in demand response program (DRP). proposed model utilizes probability density functions (PDFs) for modeling uncertainties Roulette wheel (RW) method scenario selection. simulations, carried out MATLAB, encompass two different sections. In first part, accuracy efficiency were validated by solving standard DTLZ benchmark comparing results with those several other algorithms. second was using model, solved hMOPSO‐LSA both without flexible their inclusion. To provide comprehensive evaluation, problem compared against three algorithms: flower pollination (MOFPA), MOPSO, dragonfly (MODA). demonstrate that achieves higher findings indicate DRP 6.43% 8.21% emissions. Additionally, P2G proves effective reduction, contributing 6.87% required gas supply within MG.

Язык: Английский

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An improved multi-objective particle swarm optimization algorithm for the design of foundation pit of rail transit upper cover project

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 26, 2025

In this study, a multi-objective particle swarm optimization (MOIPSO) algorithm is proposed to address complex problems, including real-world engineering challenges. The retains the basic convergence mechanism of (PSO) as its core, while innovatively combining fast non-dominated sorting technique effectively evaluate and approximate Pareto optimal solution set. To enhance diversity generalization set, crowding distance introduced, ensuring good balance between multiple objectives wider coverage space. Additionally, an acceleration factor based on trigonometric functions adaptive Gaussian mutation strategy are incorporated, improving exploration ability particles in search space facilitating their movement towards global more effectively. performance verified using multi-modal benchmark function set provided by CEC2020, comparisons made with five advanced metaheuristics. MOIPSO also applied solve design problem rail transit upper cover foundation pit, further demonstrating practical effectiveness algorithm. results show that not only performs well testing but proves highly competitive solving problems. Note source codes MOGWO publicly available at https://au.mathworks.com/matlabcentral/fileexchange/177404-moipso-optimization-engineering-problem .

Язык: Английский

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A Comprehensive Review of Metaheuristics for Hyperparameter Optimization in Machine Learning

Опубликована: Март 29, 2024

Hyperparameter optimization is a critical step in the development and fine-tuning of machine learning (ML) models. Metaheuristic techniques have gained significant popularity for addressing this challenge due to their ability search hyperparameter space efficiently. In review, we present detailed analysis various metaheuristic ML, encompassing population-based, single solution-based, hybrid approaches. We explore application metaheuristics Bayesian neural architecture search, two prominent areas within field. Moreover, provide comparative these based on established criteria evaluate performance diverse ML applications. Finally, discuss future directions open challenges with special emphasis opportunities improvement metaheuristics. Other crucial issues like adaptability new paradigms, computational complexity, scalability are also discussed critically. This review aims researchers practitioners comprehensive understanding state-of-the-art tuning, thereby facilitating informed decisions advancements

Язык: Английский

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Metaheuristic Algorithms and Their Applications in Different Fields

Опубликована: Март 29, 2024

A potent method for resolving challenging optimization issues is provided by metaheuristic algorithms, which are heuristic approaches. They provide an effective technique to explore huge solution spaces and identify close ideal or optimal solutions. iterative often inspired natural social processes. This study provides comprehensive information on algorithms the many areas in they used. Heuristic well-known their success handling issues. a tool problem-solving. Twenty such as tabu search, particle swarm optimization, ant colony genetic simulated annealing, harmony included article. The article extensively explores applications of these diverse domains engineering, finance, logistics, computer science. It underscores particular instances where have found utility, optimizing structural design, controlling dynamic systems, enhancing manufacturing processes, managing supply chains, addressing problems artificial intelligence, data mining, software engineering. paper thorough insight into versatile deployment across different sectors, highlighting capacity tackle complex wide range real-world scenarios.

Язык: Английский

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An Improved Multi-Objective Brain Storm Optimization Algorithm for Hybrid Microgrid Dispatch

International Journal of Swarm Intelligence Research, Год журнала: 2024, Номер 15(1), С. 1 - 21

Опубликована: Янв. 18, 2024

The increasing integration of renewable energy sources into microgrids has led to challenges in achieving daily optimal scheduling for hybrid alternating current/direct current (HMGs). To solve the problem, this article presents a novel AC/DC microgrid method based on an improved brain storm optimization (BSO) algorithm. Firstly, with economic and storage device health as primary objective functions, paper proposes dispatch model AC-DC microgrids. overcome limitations traditional algorithms, including premature convergence can only find non-inferior solution sets, multi-objective BSO algorithm that integrates learning selection strategies. Additionally, fuzzy decision-making is employed achieve dispatching select most suitable compromise solution. Finally, experiments are conducted verify effectiveness proposed demonstrate practicality real application scenarios.

Язык: Английский

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MORKO: A Multi-objective Runge–Kutta Optimizer for Multi-domain Optimization Problems

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 8, 2025

Abstract In the current landscape, there is a rapid increase in creation of new algorithms designed for specialized problem scenarios. The performance these unfamiliar or practical settings often remains untested. This paper presents development, multi-objective Runge–Kutta optimizer (MORKO), which built upon principles elitist non-dominated sorting and crowding distance. goal to achieve superior efficiency, diversity, robustness solutions. MORKO effectiveness further enhanced by incorporating various strategies that maintain balance between diversity execution efficiency. approach not only directs search toward optimal regions but also ensures process does become stagnant. efficiency compared against renowned like marine predicator algorithm (MOMPA), gradient-based (MOGBO), evolutionary based on decomposition (MOEA/D), genetic (NSGA-II) several test benchmarks such as ZDT, DTLZ, constraint (CONSTR, TNK, SRN, BNH, OSY KITA) real-world engineering design (brushless DC wheel motor, safety isolating transformer, helical spring, two-bar truss, welded beam, disk brake, tool spindle cantilever beam) problems. We used unique, non-overlapping metrics this comparison suggested fresh correlation analysis technique exploration. outcomes were rigorously tested confirmed using non-parametric statistical evaluations. proves excel deriving comprehensive varied solutions many tests challenges, owing its multifaceted features. Looking ahead, has potential applications complex management tasks.

Язык: Английский

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