MATLAB Implementation of Multi-Objective Optimization with Source Code

This documentation provides MATLAB source code for multi-objective optimization, serving as a reference implementation. Additionally, we expand on background knowledge and application domains of multi-objective optimization. Multi-objective optimization is a technique that seeks optimal solutions balancing multiple competing objectives. It finds applications across various fields including engineering, finance, and environmental science. The implementation typically involves key MATLAB functions such as gamultiobj from the Global Optimization Toolbox for evolutionary algorithms, or fgoalattain for goal attainment methods. Practical challenges in multi-objective optimization include proper objective function formulation and weight assignment between competing goals. The provided source code not only demonstrates practical implementation techniques but also helps users understand the fundamental concepts and challenges in multi-objective optimization, including Pareto front generation and solution selection criteria.