Multi-Objective Genetic Algorithm Using NSGA-II Method

This implementation package provides a comprehensive NSGA-II (Non-dominated Sorting Genetic Algorithm II) based multi-objective genetic algorithm solution. The algorithm features efficient non-dominated sorting, crowding distance computation, and elite preservation mechanisms to maintain population diversity while converging to Pareto-optimal solutions. The code architecture employs modular design with configurable parameters including population size, crossover/mutation rates, and termination criteria. The program utilizes tournament selection for parent selection and simulated binary crossover (SBX) with polynomial mutation for offspring generation. The implementation includes visualization capabilities for Pareto front analysis and performance metrics calculation. Researchers and practitioners can easily modify the objective functions, constraints, and genetic operators to suit specific optimization scenarios. Key functions include population initialization with constrained handling, fast non-dominated sorting with O(MN²) complexity, and crowding distance assignment for density estimation. The algorithm supports both continuous and discrete variable optimization problems. With proper parameter tuning and problem-specific adaptations, this implementation can effectively solve complex multi-objective optimization challenges in engineering design, resource allocation, and decision-making systems. The package includes comprehensive documentation on algorithm parameters and extension guidelines, making it suitable for both academic research and industrial applications requiring robust multi-objective optimization capabilities.