Multi-Objective Genetic Algorithm Using NSGA Method

This program implements a multi-objective genetic algorithm using the NSGA (Non-dominated Sorting Genetic Algorithm) approach. Designed as a generic package, the code can be customized according to specific requirements. The algorithm addresses optimization problems with multiple conflicting objectives by employing NSGA's distinctive non-dominated sorting mechanism and crowding distance computation to maintain population diversity. Key implementation features include: - Pareto-based selection using non-dominated sorting - Crowding distance calculation for diversity preservation - Configurable genetic operators (crossover and mutation) - Modular architecture allowing easy adaptation to different problem domains The program effectively finds a set of optimal solutions (Pareto front) that represent the best trade-offs between competing objectives under given constraints. Through this implementation, users can gain deeper insights into multi-objective optimization challenges and obtain robust support for research and practical applications. The object-oriented design facilitates easy extension and modification of algorithm components, making it suitable for various engineering and scientific optimization problems.