Improvement of the performance of the Quantum-inspired Evolutionary Algorithms: structures, population, operators

Population diversity is very important in giving the algorithm the power to explore the search space and not get trapped in local optima. In this respect, using a probabilistic representation for the quantum individuals, the Quantum-inspired Evolutionary Algorithms (QiEA) claim higher diversity in the population. Here, considering this important feature of QiEA, we propose different structures to offer better interaction between the q-individuals and propose new operators to preserve the diversity in the population and thus improve the performance of the QiEA. The effect of the structured population is investigated on the performance of the algorithm. Additionally, two operators are proposed in this paper. Being called the Diversity Preserving QiEA the first operator finds the converged similar q-individuals around a local optimum and while keeping the best q-individuals, by reinitializing the inferior ones pushes them out of the basin of attraction of the local optimum, so helping the algorithm to search other regions in the search space. The other operator is a reinitialization operator which by reinitializing the whole population helps it escape from the local optima it is trapped in. By studying the effect of the parameters of the proposed operators on their performance we show how the proposed operators improve the performance of QiEA. Experiments are performed on Knapsack, Trap and fourteen numerical objective functions and the results show better performance for the proposed algorithm than the original version of QiEA.