2.4 Simulated annealing In this paper, we apply a simulated annealing algorithm (SA) [8] to find a sequence-pair which gives the best floorplan and routing. In the SA algorithm, an initial solution is repeatedly improved by making small alteration until further improvement cannot be made by such alteration. Unlike greedy-type local search algorithms, the SA algorithm can avoid entrapment in a local minimum by allowing occasional uphill moves which deteriorate the objective function value. The uphill move is allowed with the probability given by ********,where T is a control parameter called the temperature, and ******* is the difference between objective function values of the current and neighborhood solution. The temperature is initially set with a certain method and gradually lowered in a predetermined method, called the cooling schedule. The most commonly used annealing schedule is called exponential cooling, which begins at some initial temperature, *** and decreases the temperature in steps according to *** where *** . Typically, a fixed number of moves must be accepted at each temperature before proceeding to the next. The algorithm terminates either when the temperature reaches some final value, *** or when some other stopping criterion has been met.