Simulated annealing algorithm with adaptive neighborhood

As we know, simulated annealing algorithm with large neighborhoods has greater probability of arriving at a global optimum than a small one has, if the other conditions, i.e., the initial configuration, initial temperature and temperature decreasing rate, are the same. However, the large neighborhood is not always beneficial, such as when the distance between the global optimum and the current solution is smaller than the step size. Therefore a simulated annealing algorithm with adaptive neighborhood is proposed in this paper. The adaptive non-uniform mutation operation borrowed from evolutionary algorithm is incorporated into simulated annealing for new solution generation. The neighborhood size reduces in probability with the progress of algorithm. It nearly covers the whole search space in the initial stage of algorithm in the sense of probability. The search engine only searches a very local neighborhood at the later stage of algorithm. Why to hybridize non-uniform mutation with simulated annealing is also analyzed and demonstrated. The numerical experiments show that the hybridization can greatly enhance the performance and the reliability of simulated annealing algorithm. Further experiments are made for benchmarks with expanding search domains. Satisfiable results are obtained again even if the variable bounds are enlarged to 1000 times. Theoretical analysis and simulation experiments illustrate the consistent excellent performance and the possible application of nonu-SA algorithm. (C) 2010 Elsevier B.V. All rights reserved.