A New Dynamic Simulated Annealing Algorithm for Global Optimization

Many problems in system analysis in real world lead to continuous-domain optimization. Existence of sophisticated and many-variable problems in this field emerge need of efficient optimization methods. One of the optimization algorithms for multi-dimensional functions is simulated annealing (SA). In this paper, a modified simulated annealing named Dynamic Simulated Annealing (DSA) is proposed which dynamically switch between two types of generating function on traversed path of continuous Markov chain. Our experiments indicate that this approach can improve convergence and stability and avoid delusive areas in benchmark functions better than SA without any extra mentionable computational cost.