A Cuckoo Search algorithm for multimodal optimization

An multi-modal optimization algorithm based on a evolutionary approach

Interest in multimodal optimization is expanding rapidly since many practical engineering problems demand the localization of multiple optima within a search space. Multimodal optimization requires a good balance between exploration and exploration of the population individuals in hyperspace so that all the local and global attractors can be successfully and accurately detected. The cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which is inspired by the breeding behavior of some cuckoo species. One of the most powerful features of CS is the use of Lévy flights to generate new candidate solutions. Under this approach, candidate solutions are modified by employing many small changes and occasionally large jumps. As a result, CS can substantially improve the relationship between exploration–exploitation, still enhancing its search capabilities. Despite of such characteristics, the CS method still fails in providing multiple solutions in a single execution. In order to overcome such inconvenience, this paper proposes a new multimodal optimization algorithm called the Multi-modal Cuckoo Search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of: (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions; (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima; and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourth teen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration.
The article was published in:
Erik Cuevas and Adolfo Reyna-Orta, A Cuckoo Search algorithm for multimodal optimization, The Scientific World Journal, Volume 2014 (2014), Article ID 497514, 20 pages

http://www.hindawi.com/journals/tswj/2014/497514/

MATLAB SOFTWARE

The function mCuckoo.m implements an multi-modal optimization example which can be modified.