Clonal Selection Algorithm And Its Application In High-Dimensional Global Function Optimization

Biological immune system is an adaptive system, which can learn, memory, identify and extract feature. The system can effectively recognize and eliminate invading antigens. Artificial immune algorithm (AIS) is a novel computational framework inspired by biological immune systems. Clonal Selection Algorithm (CSA) is one of important branches of AIS, and it is used to solving difficult high-dimensional optimization problems. Clonal selection and hypermutation mechanisms are its main characteristics.According to the features of higher-dimensional Rotated and shifted functions, this paper presents a novel heuristic algorithm, name Greedy Immune Memory CSA, to prevent the evolutionary stagnation of the population. The immune memory mechanism is employed to strength the exploitation ability of population. Meanwhile, a multi-mutation strategy and a multi-round competition are adopted. A serial experiment carries out through the testing suit of CEC2005, and the results show the presented algorithm improves the solution quality.To deal with the immune algorithmic defect on global searching, we develop a new algorithm, called DECSA. The novel algorithm integrates differential evolution to CSA in order to information fusion among antibodies. The offspring individuals inherit evolutionary information of multiple parents so that the diversity of the population could be enriched. Experiments indicate that the novel algorithm has a strong ability in global research.In order to strengthen the explore ability of evolutionary population, an immune memory mechanism is used in DECSA. The novel algorithm employs the diversity immune memory mechanism. Regarding DE as a mutation operator and triple control parameters are main characteristics of the new DECSA. Experiments show the new DECSA outperform the previous version in term of solution quality.