Optimal Anytime Constrained Simulated Annealing for Constrained Global Optimization

Abstract. In this paper we propose an optimal anytime version of constrained simulated annealing (CSA) for solving constrained nonlinear programming problems (NLPs). One of the goals of the algorithm is to generate feasible solutions of certain prescribed quality using an average time of the same order of magnitude as that spent by the original CSA with an optimal cooling schedule in generating a solution of similar quality. Here, an optimal cooling schedule is one that leads to the shortest average total number of probes when the original CSA with the optimal schedule is run multiple times until it nds a solution. Our second goal is to design an anytime version of CSA that generates gradually improving feasible solutions as more time is spent, eventually nding a constrained global minimum (CGM). In our study, we have observed a monotonically non-decreasing function relating the success probability of obtaining a solution and the average completion time of CSA, and an exponential funct...

• Type: Conference
• Year: 2000
• Where: CP
• Authors: Benjamin W. Wah, Yixin Chen
• Keywords: Artificial Intelligence, Average Completion Time, CP 2000, Optimal Cooling Schedule, Original Csa