Memetic Algorithm with Local Search Chaining for Continuous Optimization Problems: A Scalability Test

Memetic algorithms arise as very effective algorithms to obtain reliable and high accurate solutions for complex continuous optimization problems. Nowadays, higher dimensional optimization problems are an interesting field of research, that introduces new problems for the optimization process, making recommendable to test the scalable capacities of optimization algorithms. In particular, in memetic algorithms, a higher dimensionality increases the domain space around each solution, requiring that the local search method must be applied with a high intensity. In this work, we present a preliminar study of a memetic algorithm that assigns to each individual a local search intensity that depends on its features, by chaining different local search applications. This algorithm has obtained good results in continuous optimization problems and we study whether, using this intensity adaptation mechanism with the scalable LS method MTS-LS2, the algorithm is scalable enough for being a good algorithm for medium and high-dimensional problems. Experiments are carried out to test the ability of being scalable, and results obtained show that the proposal is scalable in many of the functions, scalable and non-scalable, of the benchmark used.