A massively parallel SIMD algorithm for combinatorial optimization

Many significant engineering and scientific problems involve optimization of some criteria over a combinatorial configuration space. The two methods most often used to solve these problems effectively-simulated annealing (SA) and genetic algorithms (GA)-do not easily lend themselves to massive parallel implementations. This paper introduces a new hybrid algorithm that inherits those aspects of GA that lend themselves to parallelization, and avoids serial bottlenecks of GA approaches by incorporating elements of SA to provide a completely parallel, easily scalable hybrid GA/SA method. This new method, called genetic simulated annealing, does not require parallelization of any problem specific portions of a serial implementation-existing serial implementations can be incorporated as-is. Results of a study on two difficult combinatorial optimization problems, a 100 city traveling salesperson problem and a 24 word, 12 bit error correcting code design problem, performed on a 16 K PE MasPar MP-1, indicate significant advantages of the method. One of the key results is that the performance of the algorithm scales up almost linearly with the increase of processing elements. Additionally, the algorithm does not require careful choice of control parameters, a significant advantage over SA and GA.