# MATLAB Homework Solutions

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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

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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

1. 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;

1. 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);

1. 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);

1. 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);

1. 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;

1. 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);

1. 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);

1. 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);

1. 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);

1. 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

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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);

1. 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.

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