Adaptive evolutionary optimization algorithm for problems with hybrid indices

Problems with hybrid indices are common, but the performance of previous methods in solving these problems needs to be further improved. We propose an adaptive evolutionary optimization algorithm of solving the above problems effectively in this study. First, the convergence rate of a population is calculated based on the distance between the optimal individuals with adjacent generations; then the crossover and mutation probabilities are adjusted dynamically according to the diversity, the convergence rate of a population and the number of generations. We apply the proposed algorithm to an interior layout design problem, a typical optimization problem with hybrid indices, and compare it with the algorithm with constant crossover and mutation probabilities. The experimental results confirm that the proposed algorithm has advantages in the number, quality and distribution of optimal solutions. The proposed algorithm has a good tradeoff between exploration and exploitation, and provides an efficient way to solve an optimization problem with hybrid indices.