Multi-Objective Optimization Example Using Genetic Algorithm

The toolbox includes a well-documented example demonstrating multi-objective optimization using genetic algorithms. This example serves as an excellent starting point for understanding the fundamental concepts and methodology behind genetic algorithm-based multi-objective optimization. The implementation showcases how to optimize multiple conflicting objectives simultaneously, rather than focusing on a single objective function. The example provides practical MATLAB code demonstrating key genetic algorithm components including population initialization, fitness assignment using Pareto dominance, selection operators, crossover and mutation operations, and elitism preservation. It illustrates advanced techniques such as non-dominated sorting and crowding distance calculation for maintaining population diversity. Through this implementation, users can learn effective strategies for handling trade-offs between competing objectives and visualizing Pareto-optimal solutions. For developers seeking to deepen their understanding of multi-objective optimization with genetic algorithms, this example serves as a valuable educational resource with ready-to-use code templates and implementation best practices.