Clustering Algorithm Based on Genetic Simulated Annealing Optimization

In the initial stages of genetic algorithm execution, substantial individual diversity exists. When employing classical roulette wheel selection, the number of offspring generated is proportional to parental fitness values. This frequently leads to premature convergence where descendants of a few superior individuals rapidly dominate the entire population. During later algorithm phases, fitness values converge toward uniformity, reducing the reproductive advantage of elite individuals and causing evolutionary stagnation. Implementing appropriate fitness scaling becomes essential - during high-temperature phases (early genetic algorithm stages), individuals with similar fitness values maintain comparable offspring probabilities. As temperature gradually decreases, the scaling effect intensifies, amplifying fitness differences among similarly-fit individuals and enhancing the selective advantage of elite solutions. The integration of simulated annealing and genetic algorithms creates a synergistic system that effectively addresses traditional genetic algorithms' premature convergence limitations. For clustering applications, the algorithm incorporates specialized genetic encoding schemes and fitness function designs tailored to cluster characteristics. This customization enables more efficient and rapid convergence toward global optima. This case study provides comprehensive analysis of the genetic simulated annealing-based clustering algorithm, detailing fundamental principles, operational workflows, and implementation methodologies. The research includes performance benchmarking and comparative assessments, validating the algorithm's effectiveness and superiority in solving clustering problems. Key implementation aspects involve chromosome encoding using cluster centroid coordinates, fitness computation via within-cluster sum of squares, and adaptive cooling schedules for annealing temperature management.