Obtaining the true Pareto front typically involves using mathematical or analytical methods to compute the optimal solutions that represent the ideal trade-offs between multiple conflicting objectives. In many real-world problems, the true Pareto front is not known analytically, and its computation can be challenging or even impossible.
But if you have, true Pareto front - then use this code to plot
% Replace this with the true Pareto front data (matrix with objective values)
true_pareto_front = [
% Objective 1, Objective 2, ... (Add more columns if you have more objectives)
0.1, 0.8;
0.2, 0.7;
0.3, 0.6;
0.4, 0.5;
0.5, 0.4;
0.6, 0.3;
0.7, 0.2;
0.8, 0.1;
];
% Replace this with the Pareto front obtained from ABC algorithm (matrix with objective values)
abc_pareto_front = [
% Objective 1, Objective 2, ... (Add more columns if you have more objectives)
0.1, 0.75;
0.2, 0.72;
0.3, 0.68;
0.4, 0.55;
0.5, 0.42;
0.6, 0.31;
0.7, 0.21;
0.8, 0.1;
];
% Plot the true Pareto front and the ABC Pareto front
figure;
hold on;
plot(true_pareto_front(:, 1), true_pareto_front(:, 2), 'bo', 'MarkerSize', 10);
plot(abc_pareto_front(:, 1), abc_pareto_front(:, 2), 'r+', 'MarkerSize', 10);
hold off;
% Add labels and legend
xlabel('Objective 1');
ylabel('Objective 2');
legend('True Pareto Front', 'ABC Pareto Front');
title('Comparison of True Pareto Front and ABC Pareto Front');
