A bio inspired clonal algorithm with estimationof distribution algorithm for global optimization

This paper introduces a new bio-inspired algorithm for global optimization that combines the quantum computing concepts with the immune clonal selection and vaccination principles. It employs the estimation of distribution algorithm (EDA) to optimize the vaccines by estimating and sampling the probabilistic model of the fittest ones. The proposed algorithm is implemented and evaluated using standard benchmark test problems where the obtained results carried out that it is performing well in terms of the solutions quality and diversity. Results are also compared with their corresponding of other methods in literature, the quantum inspired immune clonal algorithm (QICA) and the QICA - with vaccine algorithm, where the proposed algorithm is superior to both of them. The algorithm is able to improve the solutions diversity and maintain high quality solutions in addition to its ability to avoid falling in local optimum for multi modal problems.