Region based memetic algorithm for real-parameter optimisation

Memetic algorithms with an appropriate trade-off between the exploration and exploitation can obtain very good results in continuous optimisation. That implies the evolutionary algorithm should be focused in exploring the search space while the local search method exploits the achieved solutions. To tackle this issue, we propose to maintain a higher diversity in the evolutionary algorithm’s population by including a niching strategy in the memetic algorithm framework. In this work, we design a novel niching strategy where the niches divide the search space into hypercubes of equal size called regions forbidding the presence of two solutions in each region. The objective is to avoid the competition between the local search and the evolutionary algorithm. We tested this niching strategy in a memetic algorithm with local search chaining and obtained significant improvements. The resulting model also appeared to be very competitive with state-of-the-art algorithms.