Quantum-Inspired Immune Clonal Optimization

This paper proposes a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QICA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, instead of binary, numeric, and symbolic representation, QICA uses a quantum bit, defined as the smallest unit of information, for the probabilistic representation and uses a string of quantum bits as a quantum bit individual. By quantum mutation operator, we can make full use of the information of the current best individual to perform the next search for speeding up the convergence. To demonstrate its effectiveness and applicability, experiments are carried out on premature convergence. Secondly, owing to the novel unconstrained benchmark problems and multiuser detection in direct-sequence code-division multiple-access (DS-CDMA) system. The results show that QICA is superior to the other algorithms in quality and efficiency