Artificial Immune Clonal Selection Algorithm: A Novel Intelligent Optimization Approach

The Artificial Immune Clonal Selection Algorithm discussed here represents a relatively recent development in intelligent algorithms. While its core algorithmic structure bears close resemblance to genetic algorithms, it employs specialized design principles when addressing network node grouping and scheduling problems. The algorithm's objective is to solve optimization challenges by simulating immune system mechanisms, implementing key functions such as antigen recognition, clonal expansion of high-affinity antibodies, and affinity-based selection. Through iterative cloning and selection processes, the algorithm continuously refines problem solutions to achieve enhanced performance. This approach typically involves population initialization, affinity calculation, cloning operation with mutation rates proportional to affinity, and reselection of optimal candidates. The algorithm finds extensive applications across various domains including logistics scheduling, task allocation, and resource optimization. Consequently, the Artificial Immune Clonal Selection Algorithm demonstrates significant potential as a promising optimization technique worthy of further research and practical implementation.