Hi @Daniel,
Antlion optimization algorithm direct use for fuel cost optimization in a thermal power plant may not be the most suitable choice due to the specific requirements and constraints of such a complex system. I will consider alternative optimization techniques like Particle Swarm Optimization, or Simulated Annealing to be considered more appropriate. These methods are commonly used in engineering applications and can handle the intricacies of thermal power plant optimization more effectively. I will consider exploring these alternative optimization algorithms tailored to the specific needs of fuel cost optimization in thermal power plants for better results. I can provide example of implementing PSO using the built-in functions or toolboxes specifically designed for optimization tasks. Here is a simple example of how you can use the PSO algorithm in MATLAB for fuel cost optimization:
% Define the objective function (fuel cost function)
fun = @(x) your_fuel_cost_function(x);
% Define the number of variables and constraints
nvars = number_of_variables;
lb = lower_bounds;
ub = upper_bounds;
% Perform PSO optimization
options = optimoptions('particleswarm', 'SwarmSize', 100, 'MaxIterations', 100);
[x, fval] = particleswarm(fun, nvars, lb, ub, options);
% Display the optimized results
disp(['Optimized fuel cost: ' num2str(fval)]);
disp(['Optimal solution: ' num2str(x)]);
So, in this code snippet, you need to replace your_fuel_cost_function with the actual function that calculates the fuel cost based on the input variables. Additionally, specify the appropriate number of variables, lower and upper bounds for the variables, and customize the optimization options according to your problem requirements. For more information on particleswarm function, please refer to
https://www.mathworks.com/help/gads/particleswarm.html?searchHighlight=particleswarm&s_tid=srchtitle_support_results_1_particleswarm
Hope, this will help you get started, please let me know if you have any further questions.
