Function Optimization (With or Without Constraints) and Combinatorial Optimization Algorithms

This article comprehensively explores the concepts and underlying principles of function optimization, which can be effectively applied to both constrained and unconstrained optimization problems. Furthermore, we present detailed explanations of combinatorial optimization algorithms along with their complete source code implementations. These highly efficient algorithms are designed for widespread application in real-world problem-solving scenarios. The provided source code includes key functions for optimization processes, such as objective function evaluation, constraint handling mechanisms, and solution space exploration techniques. For researchers interested in evolutionary computation methods, we have included practical genetic algorithm examples that demonstrate population initialization, fitness evaluation, selection operators, and crossover/mutation implementations. These examples serve as valuable resources for understanding algorithm applications and performance advantages in optimization tasks.