MATLAB Homework Solutions are aided by us, for all levels. You can contact us we give you best project results. Drop us all your project details to guide you more in MATLAB all our developers are trained experts who give you detailed support. MATLAB is examined as a robust platform as well as an efficient programming language, which is utilized across numerous fields. To solve several types of optimization issues based on various domains, we offer an outline in an explicit and concise manner, along with instances of MATLAB code snippets:
Engineering Applications
- Structural Design Optimization
- To reduce weight in addition to preserving stability, we employ Particle Swarm Optimization (PSO) or Genetic Algorithms (GA).
- Sample Code:
% Define objective function for truss structure optimization
objectiveFunction = @(x) calculateWeight(x); % Custom function to calculate weight
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestDesign, minWeight] = ga(objectiveFunction, numVariables, [], [], [], [], lb, ub, @constraintsFunction, options);
- PID Controller Tuning
- In order to adapt the parameters of the PID controller, utilize PSO.
- Sample Code:
% Define the objective function for PID tuning
objectiveFunction = @(K) pidPerformance(K, plantModel); % Custom function to evaluate performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestK, bestPerformance] = particleswarm(objectiveFunction, 3, lb, ub, options);
- Antenna Design Optimization
- By means of Differential Evolution (DE), the antenna parameters have to be improved.
- Sample Code:
% Define objective function for antenna design
objectiveFunction = @(x) antennaPerformance(x); % Custom function to evaluate antenna performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestDesign, bestPerformance] = ga(objectiveFunction, numVariables, [], [], [], [], lb, ub, [], options);
- Heat Exchanger Design
- For high effectiveness, the parameters of a heat exchanger must be enhanced with PSO technique.
- Sample Code:
% Define objective function for heat exchanger optimization
objectiveFunction = @(x) heatExchangerEfficiency(x); % Custom function to evaluate efficiency
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestDesign, maxEfficiency] = particleswarm(objectiveFunction, numVariables, lb, ub, options);
- Vehicle Suspension System Design
- Specifically for ride convenience, we enhance suspension framework parameters through the GA approach.
- Sample Code:
% Define objective function for suspension system design
objectiveFunction = @(x) suspensionComfort(x); % Custom function to evaluate comfort
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestDesign, bestComfort] = ga(objectiveFunction, numVariables, [], [], [], [], lb, ub, [], options);
- Renewable Energy System Optimization
- The parameters of renewable energy frameworks have to be improved by utilizing PSO technique.
- Sample Code:
% Define objective function for renewable energy system optimization
objectiveFunction = @(x) renewableEnergyPerformance(x); % Custom function to evaluate performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParameters, maxPerformance] = particleswarm(objectiveFunction, numVariables, lb, ub, options);
- Wireless Sensor Network Deployment
- Through the use of GA, the deployment of wireless sensor nodes has to be enhanced.
- Sample Code:
% Define objective function for sensor network deployment
objectiveFunction = @(x) sensorNetworkCoverage(x); % Custom function to evaluate coverage
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestPlacement, maxCoverage] = ga(objectiveFunction, numVariables, [], [], [], [], lb, ub, [], options);
- Electric Motor Design
- For effectiveness, we improve the parameters of an electric motor with the PSO method.
- Sample Code:
% Define objective function for electric motor optimization
objectiveFunction = @(x) motorEfficiency(x); % Custom function to evaluate efficiency
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestDesign, maxEfficiency] = particleswarm(objectiveFunction, numVariables, lb, ub, options);
- Optimal Power Flow (OPF) in Electrical Grids
- In addition to preserving framework strength, reduce generation expenses by means of DE.
- Sample Code:
% Define objective function for OPF
objectiveFunction = @(x) powerFlowCost(x); % Custom function to evaluate cost
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestSettings, minCost] = ga(objectiveFunction, numVariables, [], [], [], [], lb, ub, @constraintsFunction, options);
- Supply Chain Network Design
- Particularly for cost effectiveness, the supply chain networks should be improved through PSO technique.
- Sample Code:
% Define objective function for supply chain optimization
objectiveFunction = @(x) supplyChainCost(x); % Custom function to evaluate cost
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestNetwork, minCost] = particleswarm(objectiveFunction, numVariables, lb, ub, options);
Machine Learning and Data Science
- Hyperparameter Tuning for Machine Learning Models
- The hyperparameters of machine learning models have to be adapted by employing Bayesian Optimization.
- Sample Code:
% Define objective function for hyperparameter tuning
objectiveFunction = @(params) modelPerformance(params); % Custom function to evaluate performance
results = bayesopt(objectiveFunction, variableDefinitions, ‘MaxObjectiveEvaluations’, 30);
bestHyperparameters = results.XAtMinObjective;
- Feature Selection for Classification Problems
- For the categorization process, we plan to choose the highly important characteristics with the aid of GA.
- Sample Code:
% Define objective function for feature selection
objectiveFunction = @(features) classificationAccuracy(features); % Custom function to evaluate accuracy
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestFeatures, maxAccuracy] = ga(objectiveFunction, numFeatures, [], [], [], [], lb, ub, [], options);
- Neural Network Architecture Optimization
- Through the utilization of GA, the neural network design has to be enhanced.
- Sample Code:
% Define objective function for neural network architecture optimization
objectiveFunction = @(architecture) neuralNetworkPerformance(architecture); % Custom function to evaluate performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestArchitecture, bestPerformance] = ga(objectiveFunction, numLayers, [], [], [], [], lb, ub, [], options);
- Clustering Algorithm Optimization
- The parameters of various clustering methods such as K-means must be improved with the PSO approach.
- Sample Code:
% Define objective function for clustering optimization
objectiveFunction = @(params) clusteringPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, bestPerformance] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Training Deep Learning Models
- With the aim of enhancing the training operation of deep learning models, our project employs Bayesian Optimization.
- Sample Code:
% Define objective function for training optimization
objectiveFunction = @(params) deepLearningPerformance(params); % Custom function to evaluate performance
results = bayesopt(objectiveFunction, variableDefinitions, ‘MaxObjectiveEvaluations’, 30);
bestTrainingParams = results.XAtMinObjective;
- Optimization of Recommender Systems
- In order to improve the recommender frameworks’ parameters, we implement PSO technique.
- Sample Code:
% Define objective function for recommender system optimization
objectiveFunction = @(params) recommenderPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, bestPerformance] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Time Series Forecasting Model Optimization
- The parameters of time series prediction models should be enhanced by means of DE.
- Sample Code:
% Define objective function for time series forecasting optimization
objectiveFunction = @(params) forecastingPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, bestPerformance] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Ensemble Learning Optimization
- In ensemble learning approaches, the optimal integration of models has to be identified by utilizing GA.
- Sample Code:
% Define objective function for ensemble learning optimization
objectiveFunction = @(weights) ensemblePerformance(weights); % Custom function to evaluate performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestWeights, bestPerformance] = ga(objectiveFunction, numModels, [], [], [], [], lb, ub, [], options);
- Data Preprocessing Optimization
- To enhance data preprocessing parameters and methods, our project implements PSO.
- Sample Code:
% Define objective function for data preprocessing optimization
objectiveFunction = @(params) preprocessingPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, bestPerformance] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Genetic Algorithm vs. PSO Comparison
- On different optimization issues, the performance of PSO and GA must be compared.
- Sample Code:
% Define objective function for comparison
objectiveFunction = @(x) optimizationProblem(x); % Custom function to evaluate performance
% GA optimization
gaOptions = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestSolutionGA, bestFitnessGA] = ga(objectiveFunction, numVariables, [], [], [], [], lb, ub, [], gaOptions);
% PSO optimization
psoOptions = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestSolutionPSO, bestFitnessPSO] = particleswarm(objectiveFunction, numVariables, lb, ub, psoOptions);
% Compare results
disp([‘GA Best Fitness: ‘, num2str(bestFitnessGA)]);
disp([‘PSO Best Fitness: ‘, num2str(bestFitnessPSO)]);
Finance and Economics
- Portfolio Optimization
- To reduce risk and increase profit, the properties have to be assigned in a financial portfolio by employing GA.
- Sample Code:
% Define objective function for portfolio optimization
objectiveFunction = @(weights) -sharpeRatio(returns, weights); % Custom function to calculate Sharpe ratio
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestWeights, bestSharpeRatio] = ga(objectiveFunction, numAssets, [], [], [], [], lb, ub, @constraintsFunction, options);
- Option Pricing Using Optimization
- In option pricing models, we intend to enhance parameters through the utilization of Simulated Annealing (SA).
- Sample Code:
% Define objective function for option pricing optimization
objectiveFunction = @(params) optionPricingError(params); % Custom function to evaluate pricing error
options = optimoptions(‘simulannealbnd’, ‘MaxIterations’, 1000);
[bestParams, minError] = simulannealbnd(objectiveFunction, initialParams, lb, ub, options);
- Algorithmic Trading Strategy Optimization
- For enhanced efficiency, the trading policies must be improved with the PSO method.
- Sample Code:
% Define objective function for trading strategy optimization
objectiveFunction = @(params) tradingStrategyPerformance(params); % Custom function to evaluate strategy performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, bestPerformance] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Credit Scoring Model Optimization
- Particularly for improved risk evaluation, enhance the credit scoring models’ parameters by means of GA.
- Sample Code:
% Define objective function for credit scoring optimization
objectiveFunction = @(params) creditScoringAccuracy(params); % Custom function to evaluate accuracy
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, maxAccuracy] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Risk Management Optimization
- In finance sectors, improve risk handling policies by utilizing DE.
- Sample Code:
% Define objective function for risk management optimization
objectiveFunction = @(params) riskManagementPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, bestPerformance] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Economic Load Dispatch in Power Systems
- Especially in power frameworks, the economic load dispatch has to be enhanced with the approach of PSO.
- Sample Code:
% Define objective function for economic load dispatch
objectiveFunction = @(params) loadDispatchCost(params); % Custom function to evaluate cost
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, minCost] = particleswarm(objectiveFunction, numGenerators, lb, ub, options);
- Supply Chain Optimization
- For cost effectiveness, we aim to improve supply chain plans through GA technique.
- Sample Code:
% Define objective function for supply chain optimization
objectiveFunction = @(params) supplyChainCost(params); % Custom function to evaluate cost
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, minCost] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Inventory Management Optimization
- As a means to enhance stock ranges and reorder points, utilize PSO technique.
- Sample Code:
% Define objective function for inventory management optimization
objectiveFunction = @(params) inventoryCost(params); % Custom function to evaluate cost
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, minCost] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Energy Market Simulation
- In energy markets, the bidding policies should be simulated and improved by implementing GA.
- Sample Code:
% Define objective function for energy market simulation
objectiveFunction = @(params) marketPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, bestPerformance] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Financial Time Series Analysis
- The parameters of financial time series models must be enhanced through the use of DE.
- Sample Code:
% Define objective function for time series analysis
objectiveFunction = @(params) timeSeriesForecasting(params); % Custom function to evaluate forecasting accuracy
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, bestAccuracy] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
Health and Biomedical Applications
- Medical Image Segmentation
- For medical imaging, we enhance the image segmentation methods’ parameters by utilizing PSO.
- Sample Code:
% Define objective function for image segmentation optimization
objectiveFunction = @(params) segmentationAccuracy(params); % Custom function to evaluate segmentation accuracy
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, bestAccuracy] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Drug Formulation Optimization
- Specifically for effectiveness and less side effects, the combination of drug preparations has to be improved with GA approach.
- Sample Code:
% Define objective function for drug formulation optimization
objectiveFunction = @(params) drugEfficacy(params); % Custom function to evaluate drug efficacy
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestFormulation, bestEfficacy] = ga(objectiveFunction, numIngredients, [], [], [], [], lb, ub, [], options);
- Optimization of Diagnostic Systems
- To attain better preciseness, the parameters of diagnostic frameworks must be enhanced through the method of PSO.
- Sample Code:
% Define objective function for diagnostic system optimization
objectiveFunction = @(params) diagnosticAccuracy(params); % Custom function to evaluate diagnostic accuracy
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, bestAccuracy] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Genetic Network Optimization
- For enhanced interpretation of biological operations, focus on improving genetic networks by means of GA.
- Sample Code:
% Define objective function for genetic network optimization
objectiveFunction = @(params) networkPerformance(params); % Custom function to evaluate network performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestNetwork, bestPerformance] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Biomedical Signal Processing
- In order to deal with biomedical signals such as EEG and ECG, enhance signal processing methods with PSO technique.
- Sample Code:
% Define objective function for signal processing optimization
objectiveFunction = @(params) signalProcessingAccuracy(params); % Custom function to evaluate processing accuracy
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestParams, bestAccuracy] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Treatment Planning in Radiotherapy
- To focus on tumors in an effective manner, the treatment strategies should be improved in radiotherapy by employing GA.
- Sample Code:
% Define objective function for radiotherapy optimization
objectiveFunction = @(params) treatmentEfficacy(params); % Custom function to evaluate treatment efficacy
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestPlan, bestEfficacy] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Optimization of Prosthetic Devices
- For better performance, we improve the regulation and model of prosthetic devices through the approach of PSO.
- Sample Code:
% Define objective function for prosthetic optimization
objectiveFunction = @(params) prostheticPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestDesign, bestPerformance] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Health Monitoring System Optimization
- As a means to accomplish enhanced battery durability and preciseness, improve the health tracking frameworks’ parameters with the aid of GA.
- Sample Code:
% Define objective function for health monitoring optimization
objectiveFunction = @(params) monitoringAccuracy(params); % Custom function to evaluate accuracy
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, bestAccuracy] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Personalized Medicine
- On the basis of patient data, the customized treatment strategies have to be enhanced by means of PSO.
- Sample Code:
% Define objective function for personalized medicine optimization
objectiveFunction = @(params) treatmentOutcome(params); % Custom function to evaluate treatment outcome
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestTreatment, bestOutcome] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Epidemiological Modeling
- For improved disease forecasting and regulation, the parameters of epidemiological models must be enhanced through GA.
- Sample Code:
% Define objective function for epidemiological modeling optimization
objectiveFunction = @(params) modelAccuracy(params); % Custom function to evaluate model accuracy
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestParams, bestAccuracy] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
Environmental and Sustainability
- Water Distribution Network Optimization
- Consider water distribution networks and enhance their process and model through the method of PSO.
- Sample Code:
% Define objective function for water network optimization
objectiveFunction = @(params) networkEfficiency(params); % Custom function to evaluate efficiency
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestDesign, maxEfficiency] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Waste Management Optimization
- In order to improve paths for waste gathering and clearance, we utilize GA approach.
- Sample Code:
% Define objective function for waste management optimization
objectiveFunction = @(params) wasteManagementCost(params); % Custom function to evaluate cost
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestRoutes, minCost] = ga(objectiveFunction, numRoutes, [], [], [], [], lb, ub, [], options);
- Sustainable Agriculture Optimization
- For less ecological effect and high production, enhance agricultural techniques by employing PSO.
- Sample Code:
% Define objective function for agriculture optimization
objectiveFunction = @(params) agriculturalYield(params); % Custom function to evaluate yield
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestPractices, maxYield] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Energy Efficiency in Buildings
- Specifically for energy effectiveness, the process and model of building frameworks must be improved with the help of GA.
- Sample Code:
% Define objective function for energy efficiency optimization
objectiveFunction = @(params) energyEfficiency(params); % Custom function to evaluate efficiency
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestDesign, maxEfficiency] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Optimization of Renewable Energy Mix
- The integration of various renewable energy sources has to be enhanced by means of PSO technique.
- Sample Code:
% Define objective function for renewable energy mix optimization
objectiveFunction = @(params) energyMixPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestMix, maxPerformance] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Carbon Footprint Reduction Strategies
- In different industries, minimize carbon footprints by creating efficient policies. For that, make use of GA approach.
- Sample Code:
% Define objective function for carbon footprint reduction
objectiveFunction = @(params) carbonReduction(params); % Custom function to evaluate reduction strategies
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestStrategies, maxReduction] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Air Quality Monitoring Network Optimization
- Focus on air quality tracking stations and improve their deployment through PSO technique.
- Sample Code:
% Define objective function for air quality monitoring optimization
objectiveFunction = @(params) monitoringCoverage(params); % Custom function to evaluate coverage
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestPlacement, maxCoverage] = particleswarm(objectiveFunction, numStations, lb, ub, options);
- Optimization of Environmental Monitoring Systems
- In ecological tracking frameworks, we enhance the process and model by utilizing GA.
- Sample Code:
% Define objective function for environmental monitoring optimization
objectiveFunction = @(params) monitoringPerformance(params); % Custom function to evaluate performance
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestSystem, maxPerformance] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
- Sustainable Urban Planning
- For viable progression, the urban planning policies must be improved with the approach of PSO.
- Sample Code:
% Define objective function for urban planning optimization
objectiveFunction = @(params) urbanSustainability(params); % Custom function to evaluate sustainability
options = optimoptions(‘particleswarm’, ‘SwarmSize’, 30, ‘MaxIterations’, 100);
[bestPlan, maxSustainability] = particleswarm(objectiveFunction, numParams, lb, ub, options);
- Green Supply Chain Management
- Particularly for viability, the supply chain processes should be enhanced through GA technique.
- Sample Code:
% Define objective function for green supply chain optimization
objectiveFunction = @(params) supplyChainSustainability(params); % Custom function to evaluate sustainability
options = optimoptions(‘ga’, ‘PopulationSize’, 50, ‘MaxGenerations’, 100);
[bestOperations, maxSustainability] = ga(objectiveFunction, numParams, [], [], [], [], lb, ub, [], options);
Relevant to different domains, we suggested numerous optimization issues, encompassing explicit outlines to solve these in an effective manner. In addition to that, some sample MATLAB code snippets are provided by us.
