Nature-inspired Optimization Algorithm Research Articles

A New Single-Parameter Bees Algorithm.

Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or "elite" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA1, a Bees Algorithm with just one parameter. BA1 eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA1 simplifies the tuning process and increases efficiency. BA1 has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.

Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.

An Integration of Adaptive Control Techniques with Portable Solar-Powered EV Charging with Particle Swarm Optimization (PSO)

Abstract: This paper presents a novel approach utilizing Particle Swarm Optimization (PSO) for enhancing the efficiency of solar-powered electric vehicle (EV) charging systems. With the increasing adoption of EVs and the imperative to transition towards renewable energy sources, optimizing the utilization of solar power for charging becomes crucial. The proposed system integrates photovoltaic (PV) panels to harness solar energy, providing a sustainable and renewable power source for EV charging. The key innovation lies in the application of PSO, a nature-inspired optimization algorithm, to dynamically adjust charging parameters based on real-time environmental conditions, grid electricity prices, and EV battery requirements. PSO offers a robust and efficient method for solving optimization problems by simulating the behavior of swarms in nature. In the context of solar-powered EV charging, the PSO algorithm iteratively adjusts charging rates and schedules to maximize solar energy utilization, minimize charging costs, and ensure optimal battery health. The integration of PSO optimization with solarpowered EV charging represents a promising step towards achieving sustainable and intelligent transportation solutions. Future research directions may include further optimization refinements, scalability studies, and real-world deployment to validate the practicality and effectiveness of the proposed approach in diverse environments and usage scenarios.

Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot.

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.

Volume Optimization for a Single-Phase Air-Core Reactor – 2D Finite Element Method and Particle Swarm Optimization Approach

This paper describes the optimization of an air core reactor using Particle Swarm Optimization (PSO) combined with the finite element analysis. The aim is to reduce material volume costs in manufacturing and maintenance. PSO is a nature-inspired optimization algorithm that simulates swarm behavior in search of the best solution in a multidimensional search space. This article employs the PSO optimization method as an initial contribution to the design of single-phase air-core reactors, suggesting the possibility of further studies comparing its efficacy with other heuristic algorithms on the subject in the future.

Piranha predation optimization algorithm (PPOA) for global optimization and engineering design problems

A new nature-inspired optimization algorithm, Piranha predation optimization algorithm (PPOA), is proposed based on the unique foraging and predation behaviors of piranhas. Briefly, PPOA consists of three optimization operations, i.e., narrowing down to tear prey, swimming in a straight line, and swimming in a spiral. In this paper, various mathematical models for simulating the behavioral operators are presented in detail to solve different optimization challenges effectively. In this paper, the performance of PPOA is rigorously tested on 23 benchmark optimization functions, CEC2017 competition test set, CEC2020 real-world engineering optimization problems and four engineering design applications to show the applicability of the algorithm in different applications. Comparison experiments with other good and advanced competitive algorithms are conducted to reveal the advantages and performance of PPOA by using performance metrics such as Wilcoxon rank sum test and Friedman mean rank. The comparative results of this paper demonstrate the effectiveness of the proposed algorithmic strategy and its potential in applying it to solving optimization real-world engineering optimization problems.

Human monkeypox disease prediction using novel modified restricted Boltzmann machine-based equilibrium optimizer

While the globe continues to struggle to recover from the devastation brought on by the COVID-19 virus's extensive distribution, the recent worrying rise in human monkeypox outbreaks in several nations raises the possibility of a novel worldwide pandemic. The symptoms of human monkeypox resemble those of chickenpox and traditional measles, with a few subtle variations like the various kinds of skin blisters. A range of deep learning techniques have demonstrated encouraging results in image-oriented tumor cell, Covid-19 diagnosis, and skin disease prediction tasks. Hence, it becomes necessary to perform the prediction of the new monkeypox disease using deep learning techniques. In this paper, an image-oriented human monkeypox disease prediction is performed with the help of novel deep learning methodology. Initially, the data is gathered from the standard benchmark dataset called Monkeypox Skin Lesion Dataset. From the collected data, the pre-processing is accomplished using image resizing and image normalization as well as data augmentation techniques. These pre-processed images undergo the feature extraction that is performed by the Convolutional Block Attention Module (CBAM) approach. The extracted features undergo the final prediction phase using the Modified Restricted Boltzmann Machine (MRBM), where the parameter tuning in RBM is accomplished by the nature inspired optimization algorithm referred to as Equilibrium Optimizer (EO), with the consideration of error minimization as the major objective function. Simulation findings demonstrate that the proposed model performed better than the remaining models at monkeypox prediction. The proposed MRBM-EO for the suggested human monkeypox disease prediction model in terms of RMSE is 75.68%, 70%, 60.87%, and 43.75% better than PSO-SVM, Xception-CBAM-Dense, ShuffleNet, and RBM respectively. Similarly, the proposed MRBM-EO for the suggested human monkeypox disease prediction model with respect to accuracy is 9.22%, 7.75%, 3.77%, and 10.90% better than PSO-SVM, Xception-CBAM-Dense, ShuffleNet, and RBM respectively.

Rapid quantitative and qualitative analysis of talcum powder in wheat flour by microwave detection combined with multivariate analysis

Rapid and accurate monitoring of talcum powder in wheat flour is of great practical importance for maintaining market order, public health and food safety. In this study, the potential of a home-made microwave detection system for rapid quantitative and qualitative detection of talcum powder adulteration in wheat flour at 2.5–11.5 GHz was investigated. Useful microwave spectral information is selected using three feature selection strategies, the bootstrapping soft shrinkage (BOSS), the multiple feature spaces ensemble with least absolute shrinkage and selection operator (MEF-LASSO), and the competitive adaptive reweighted sampling (CARS). The eXtreme Gradient Boosting (XGBoost) classification and regression models are constructed and evaluated, and three nature-inspired optimization algorithms (NIOA) are used for parameter optimization: the Osprey Optimization Algorithm (OOA), the Crested Porcupine Optimizer (CPO), and the Sparrow Search Algorithm (SSA). The experimental results show that both feature selection strategies and NIOA algorithms can effectively improve the model performance. The SSA-XGBoost classification model has the highest prediction accuracy, and its prediction accuracy and F1-score can reach 100 % and 1, respectively. The MEF-LASSO-SSA-XGBoost regression model has strong robustness. The root mean square error (RMSE) was 1.76 %, the prediction coefficient of determination (R2) was 0.98, and the ratio of performance to deviation (RPD) was 8.66. The results of this study showed that the combination of microwave technology and machine learning model with multivariate analysis method can realize the rapid and accurate determination of qualitative and quantitative talcum powder in wheat flour.

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Hybridized machine learning models for phosphate pollution modeling in water systems for multiple uses

Phosphate pollution in water bodies is a significant environmental concern, especially in regions with extensive agricultural practices. Hence, a tool for accurately assessing the phosphate concentration is essential. This research paper explores the effectiveness of machine learning (ML) models combined with nature-inspired optimization algorithms for predicting phosphate levels in water systems. The novelty consists of integrating the power of machine learning models, which have been presenting excellent performance at capturing complex relationships in environmental pollution data, with the Harris Hawks Optimizer (HHO) optimization capabilities inspired by hawks' hunting behavior. Four hybrid implementations combining the HHO were evaluated, and four feature subsets were assessed to identify the most influential variables in the modeling process. Using water quality data from Brazilian upstream watersheds, the hybrid models were trained and validated, enabling accurate and robust predictions of phosphate concentrations. The elastic net (EN) model optimized by Harris Hawk Optimizer (HHO-EN) produced the best-averaged performance among all experiments (R = 0.825, R2 = 0.670, Root Mean Square Error (RMSE) = 0.049 mg/L, Mean Absolute Error (MAE) = 0.037 mg/L). A parametric and feature importance analysis identified the most influential parameters in the contamination modeling process. Hybrid machine learning models represent a novel and efficient strategy for water quality monitoring and environmental management, supporting the preservation of aquatic ecosystems.

Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms

This study evaluates a new hybrid approach for determining homes’ heating load (HL). The crow search algorithm (CSA), heap-based optimizer (HBO), seeker optimization algorithm (SOA), political optimizer (PO), and harmony search (HS) are the five components of the suggested paradigm. A nonlinear analysis of the effects of eight independent factors on the HL was conducted using the best structure identified in each model. The assessment procedure for the HS technique in this study consisted of three parts. The appropriate population size to utilize in the first phase was found to be the one that yields the best coefficient of determination (R2) value and the lowest root mean squared error (RMSE) value. For CSA-MLP, HBO-MLP, SOA-MLP, PO-MLP, and HS-MLP, respectively, the first phase yielded R2 = 0.96473, 0.95618, 0.96931, 0.97048, and 0.96702, and RMSE = 2.57119, 2.85968, 2.40125, 2.3554, and 2.46872. A battery of tests using a range of different nNew values (between 10 and 100) was applied to the HS-MLP with a population size of 50 in the second phase. The data indicates that the most reliable results are obtained with a nNew-value of 60. For training and testing, this value has RMSE values of 2.61518 and 2.4387 and R2 values of 0.9669 and 0.96783. In the third stage, an experiment with a population size of 50 and nNew of 60 was examined using a range of HMCR values (between 0.5 and 1.4). Concerning training and testing, the results indicate that the HMCR value 1.1 produces the most reliable results; its R2 values are 0.9739 and 0.97207, and its RMSE values are 2.32691 and 2.27488. Lastly, the results demonstrate that the accuracy of the HS-MLP method has been improved by the 3-phase analysis method.

A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

Nature-inspired optimization algorithms based feature selection: Application in credit scoring

Nature-inspired algorithms as problem-solving methodologies are extremely effective in discovery of optimized solutions in multi-dimensional and multi-modal problems. Because of qualities like “self-optimization”, “flexibility” and etc., nature-inspired algorithms for problem solving are effectively optimal. Feature selection is an approach to find approximate optimal subset of the features which are more relevant towards the particular outcome. In this study, we focused on how feature selection may improve the credit scoring model’s performance for prediction. Nature-inspired algorithms are applied for feature selection to improve the predictive performance of the credit scoring model. Additionally, four benchmark credit scoring datasets collected from the UCI repository are used to test feature selection by several Nature-inspired algorithms aggregated with “Random Forest (RF)”, “Logistic Regression (LR),” and “Multi-layer Perceptron (MLP)” for classification and results are compared in terms of classification accuracy and G-measures.

Prediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptron

One of the most significant issues in urban design is energy-related CO2 emissions, which are rising quickly as cities expand. The GDP of the Central European countries (from 1990 to 2016) based on several energy sources, such as coal, oil, natural gas, and renewable energy, are used as inputs in this study. To develop a reliable predictive network considering the problem complexity, multilayer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). By applying the approaches mentioned above to the synthesis of the MLP, the recommended BBO, BHA, BSA, FSA, and SCE ensembles are obtained. A series of parametric studies are performed to improve the effectiveness of the employed models. It is found that, by combining the BBO, BHA, BSA, FSA, and SCE algorithms, the MLP's accuracy is increased. The result from this parametric analysis showed that SCE and BBO perform better than the other three algorithms as the CO2 emission was computed with the highest level of accuracy using R2 = 0.9999 and 0.9998, RMSE = 1.6781 and 2.0539 for SCE, and R2 = 0.9999 and 0.9998, RMSE = 1.8689 and 2.3833 for BBO.

Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test

The Marine Predators Algorithm (MPA) is a prominent Nature-Inspired Optimization Algorithm (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using the Lévy or Brownian approach for its foraging strategy. Despite its acclaim, the structural bias within MPA has not been thoroughly investigated, marking a significant gap in the current research. This absence of targeted research forms the core rationale behind initiating this study. Structural bias has recently been identified in NIOAs, causing the population to revisit specific regions of the search space without gaining new information. As a result, it may lead to increased computational costs and slow down the rate of convergence. Therefore, identifying structural bias is essential to better understand the search mechanism of MPA. To ascertain the presence of any structural bias, two recently introduced models are employed: the BIAS toolbox and the Generalized Signature Test. These examinations reveal a notable structural bias in MPA, predominantly towards the center of the search space. Also, possible future research directions for MPA are discussed. Our findings provide valuable insights into the search dynamics of the algorithm, fostering the development of new, unbiased, and efficient algorithms.

SHuffled Ant Lion Optimization approach with an exponentially weighted random walk strategy

Ant Lion Optimization (ALO) method is one of the population-based nature-inspired optimization algorithms which mimics the hunting strategy of antlions. ALO is successfully employed for solving many complicated optimization problems. However, it is reported in the literature that the original ALO has some limitations such as the requirement of high number of iterations and possibility of trapping to local optimum solutions, especially for complex or large-scale problems. For this purpose, the SHuffled Ant Lion Optimization (SHALO) approach is proposed by conducting two improvements in the original ALO. Performance of the proposed SHALO approach is evaluated by solving some unconstrained and constrained problems for different conditions. Furthermore, the identified results are statistically compared with the ones obtained by using the original ALO, two improved ALOs which are the self-adaptive ALO (saALO) and the exponentially weighted ALO (EALO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) approaches. Identified results indicated that the proposed SHALO approach significantly improves the solution accuracy with a mean success rate of 76% in terms of finding the global or near-global optimum solutions and provides better results than ALO (22%), saALO (25%), EALO (14%), GA (28%), and PSO (49%) approaches for the same conditions.

Improving precision and robustness in level control of coupled tank systems: A tree seed optimization and [formula omitted]-analysis approach

Background:Efficient control of liquid levels in interconnected tank systems is a fundamental challenge in various industries, including chemical processes, wastewater treatment, and manufacturing. The traditional approach to achieving precise control has relied on Proportional–Integral–Derivative (PID) controllers, whose performance largely depends on tuned parameters. However, tuning PID controllers for complex and nonlinear systems like coupled tanks remains a challenging task. In recent years, nature-inspired optimization algorithms have gained prominence in control system design. The tree seed optimization (TSO), inspired by the dispersion of tree seeds in search of optimal growth conditions, has shown promise in solving complex optimization problems. This study seeks to explore the application of TSO in tuning PID controllers for coupled tank systems. Methodology:This research employs the TSO to optimize PID controller parameters for enhanced liquid-level control in coupled tank systems. The optimization process involves integrating performance metrics and closed-loop gain constraints to achieve optimal control of coupled tank systems. The imposed constraints aim to ensure robustness and system stability in the face of uncertainties and disturbances. Besides, μ analysis quantifies the robustness of the system by assessing its ability to tolerate uncertainties. Findings:Simulation studies conducted in this research verify the efficacy of the proposed TSO-based approach for tuning PID controllers in coupled tank systems. Compared with various methods, the TSO consistently yields better control performance, reduces settling time, and minimizes overshoot. The robustness of the optimized controllers is also evaluated, showing the ability to handle varying operating conditions effectively.

Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms

Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set.

Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho.

Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems.

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving.