EVOLUTION OF GENETIC ALGORITHMS FOR MULTICRITERIA OPTIMIZATION

The article analyzes and details the evolution of genetic algorithms for solving multi-criteria optimization problems. It considers pioneering algorithms such as VEGA (Vector Evaluated Genetic Algorithm) and FFGA (Fitness Function Genetic Algorithm), identifying their key limitations, including a tendency towards specialization and a lack of mechanisms for maintaining solution diversity. Particular focus is placed on the more advanced SPEA (Strength Pareto Evolutionary Algorithm), which integrates the concept of individual strength, an external archive for storing non-dominated solutions, and a clustering procedure to ensure uniform coverage of the Pareto front. The comparative analysis demonstrates that the sequential development of methods from VEGA to SPEA effectively addressed convergence and diversity issues, establishing multi-objective evolutionary algorithms as a powerful tool for finding optimal trade-offs in complex systems with conflicting criteria.