A Local Search Algorithm Based on Clonal Selection and Genetic Mutation for Global Optimization

The purpose of this paper is to show a local search algorithm mixing features of Hill-Climbing, Clonal Selection and Genetic Algorithms. Hill climbing is considered because only the best solution is used. Clonal Selection because the best solution is cloned. Afterwards, individuals are muted using random mutation or non-uniform mutation of genetic algorithms. Four different ways of producing neighborhood solutions have been used in the mutation operator. In the first one (HR), the number of elements are randomly chosen based on the current generation number and muted using random mutation in a certain domain. In the second one (HNU), the number of elements are randomly chosen and muted using non-uniform mutation. In the third one (HRNU), the number of elements are chosen in the same previous way, however the random mutation is used in the initial generations and non-uniform mutation is applied in the last generations. Finally (HNURT), a random number of the elements are muted based on the current generation number, using non-uniform mutation. The performance of the hybrid algorithms is evaluated by means of six multimodal benchmark functions. The results show that HNU and HNURT have better performance. A comparison between the hybrid algorithms and traditional ones, such as, evolutionary strategies, genetic algorithms, particle swarm optimization and differential evolution is presented, as well.