Abstract: The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.
Abstract: Energy consumption of a hotel can be a hot topic in
smart city; it is difficult to evaluate the contribution of impact factors
to energy consumption of a hotel. Therefore, grasping the key impact
factors has great effect on the energy saving management of a hotel.
Based on the SPIRTPAT model, we establish the identity with the
impact factors of occupancy rate, unit area of revenue, temperature
factor, unit revenue of energy consumption. In this paper, we use the
LMDI (Logarithmic Mean Divisia Index) to decompose the impact
factors of energy consumption of hotel from Jan. to Dec. in 2001. The
results indicate that the occupancy rate and unit area of revenue are the
main factors that can increase unit area of energy consumption, and the
unit revenue of energy consumption is the main factor to restrain the
growth of unit area of energy consumption. When the energy
consumption of hotel can appear abnormal, the hotel manager can
carry out energy saving management and control according to the
contribution value of impact factors.
Abstract: Genetic algorithm is widely used in optimization
problems for its excellent global search capabilities and highly parallel
processing capabilities; but, it converges prematurely and has a poor
local optimization capability in actual operation. Simulated annealing
algorithm can avoid the search process falling into local optimum. A
hybrid genetic algorithm based on simulated annealing is designed by
combining the advantages of genetic algorithm and simulated
annealing algorithm. The numerical experiment represents the hybrid
genetic algorithm can be applied to solve the function optimization
problems efficiently.