Multi-Objective Optimization using NSGA-III

This code implements a multi-objective optimization problem using the NSGA-III algorithm. It imports various libraries such as random, numpy, pandas, matplotlib, deap, json, skfuzzy, pymoo, and time. The code defines a problem, creates custom classes for fitness values and individuals, sets variable bounds, registers functions, and executes the evolutionary algorithm. The Pareto front of the resulting solutions is extracted and visualized using a scatter plot.