Quantum-inspired immune clonal algorithm

This paper proposes a new 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. QICA uses a quantum bit, defined as the smallest unit of information, for the probabilistic representation and a quantum bit individual as a string of quantum bits. In QICA, 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. Information among the subpopulation is exchanged by adopting the quantum crossover operator for improvement of diversity of the population and avoiding prematurity. We execute the proposed algorithm to solve the benchmark problems with 30,100 and 2000 dimensions and very large numbers of local minima. The result shows that the proposed algorithm can close-to-optimal solution by the less computational cost.