Multi-Objective Genetic Algorithm (MOGA) Implementation

This program implements a Multi-Objective Genetic Algorithm (MOGA) methodology to solve complex optimization problems. The implementation features a generic framework with customizable components including selection operators, crossover mechanisms, and mutation routines. Key algorithmic components include Pareto-based fitness assignment, crowding distance calculation for diversity maintenance, and elitism preservation strategies. The architecture supports flexible modification of genetic parameters such as population size, crossover probability, and mutation rates through configuration files or programmatic interfaces. The code structure employs object-oriented design patterns, separating concerns between algorithm logic, problem definition, and fitness evaluation modules. This robust toolkit enables efficient handling of various multi-objective optimization scenarios across different application domains. Whether addressing engineering design challenges, resource allocation problems, or decision analysis tasks, the program facilitates identification of Pareto-optimal solutions through evolutionary computation techniques. The implementation incorporates convergence monitoring and solution visualization capabilities to enhance analytical insights. Both novice users and experienced practitioners can leverage this package to reduce development time while obtaining accurate, reliable results. The modular design allows easy integration of custom objective functions and domain-specific constraints, making it suitable for research prototypes and production systems alike.