Variable Population Memetic Search: A Case Study on the Critical Node Problem

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size is used in such record some best solutions sampled during the search. However, given particular features problem instance under consideration, variable would be more suitable ensure search performance possible. In this work, we propose (VPMS), where strategic sizing mechanism dynamically adjust process. Our VPMS approach starts its from small only two focus on exploitation and then adapts according status continuously influence balancing between exploration. We illustrate an application challenging critical node (CNP). show that algorithm integrating population, effective local optimization procedure, backbone-based crossover operator performs very well compared state-of-the-art CNP algorithms. The able discover new upper bounds for 12 instances out 42 popular benchmark while matching 23 previous best-known bounds.