Multi-Objective Optimization Using Genetic Algorithm in MATLAB

This implementation demonstrates multi-objective optimization using genetic algorithms in MATLAB, covering both dual-objective and multi-objective function optimization scenarios. Genetic algorithms are evolutionary computation methods inspired by biological evolution principles, simulating natural selection processes through selection, crossover, and mutation operations to progressively optimize objective function values toward optimal solutions. For multi-objective optimization problems, we must consider multiple conflicting objective functions simultaneously and optimize them concurrently during the solution search process. The MATLAB implementation utilizes key functions like gamultiobj from the Global Optimization Toolbox, which employs non-dominated sorting genetic algorithms (NSGA-II) to maintain population diversity and convergence. Through genetic algorithm approaches, we can effectively identify Pareto-optimal solutions representing trade-offs between competing objectives, achieving balanced optimization results across multiple performance criteria. The implementation typically involves defining fitness functions, setting population parameters, configuring crossover and mutation rates, and analyzing the resulting Pareto front to select appropriate compromise solutions.