Dr. Harry Zhang is an Assistant Professor of the Computer Engineering and Computer Science Department at the University of Louisville. He earned his B.S. and M.S. degrees in Computer Science from Zhejiang University, China in 1999 and 2002 respectively, and Ph.D. degree from Indiana University in 2009. Dr. Zhang directs the Visualization and Intensive Graphics Lab.
Simulated annealing (SA) algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. To overcome such intrinsic limitations, we present a multi-agent SA algorithm with instance-based sampling (MSA-IBS) by exploiting learning ability of instance-based search algorithm to solve travelling salesman problem (TSP). In MSA-IBS, a population of agents run SA algorithm collaboratively. Agents generate candidate solutions with the solution components of instances in current population. MSA-IBS achieves significant better intensification ability by taking advantage of learning ability from population-based algorithm, while the probabilistic accepting criterion of SA keeps MSA-IBS from premature stagnation effectively. By analysing the effect of initial and end temperature on finite-time behaviours of MSA-IBS, we test the performance of MSA-IBS on benchmark TSP problems, and the algorithm shows good trade-off between solution accuracy and CPU time.