Buffered local search for efficient memetic agent-based continuous optimization

In this paper, a memetic search in classic and agent-based evolutionary algorithms are discussed. A local search is applied in an innovative way; namely, during an agent's life and in a classic way during the course of reproduction. Moreover, in order to efficiently utilize the computing power available, an efficient mechanism based on caching parts of the fitness function in the local search is proposed. The experimental results obtained for selected high-dimensional benchmark functions (with 5000 dimensions) show the apparent advantage of the proposed mechanism.