Multi-Objective Genetic Algorithm Using SPEA Method

This program implements a multi-objective genetic algorithm using the SPEA (Strength Pareto Evolutionary Algorithm) method, serving as a general-purpose package that supports user customization. The implementation employs SPEA, a well-established multi-objective optimization algorithm that evaluates individual fitness in the solution space to identify Pareto-optimal solutions. The algorithm maintains an external archive to store non-dominated solutions and uses a strength-based fitness assignment strategy to effectively handle multiple objectives simultaneously. To enhance adaptability, you can modify various components according to your specific requirements. Key customizable elements include: adjusting algorithm parameters such as population size, crossover and mutation rates; implementing different genetic operators (selection, crossover, mutation); and incorporating custom objective functions through modular function interfaces. The code structure features a main algorithm handler that orchestrates the evolutionary process, including fitness evaluation using Pareto dominance relationships, archive management for elitism preservation, and environmental selection mechanisms. For specific optimization problems, you can modify the objective function module while maintaining the core SPEA framework. By customizing these components, you can achieve improved optimization results for different problem domains and datasets. Should you have any questions or suggestions regarding implementation details or algorithmic modifications, please feel free to contact us for technical support.