Optimizing Complex Problems by Nature's Algorithms: Simulated Annealing and Evolution Strategy - A Comparative Study

We compare two optimization algorithms which glean their heuristic from nature: simulated annealing and evolution strategy. These algorithms are applied to difficult optimization problems: finding binary sequences with low autocorrelation, calculating ground states of certain spin glass Hamiltonians, and giving the optimal tour in a traveling salesman problem. Our findings show a problem dependence of the quality of the results. Because of fundamental difficulties in the judgement of the algorithms' quality no final conclusion can be drawn, but the comparison gives valuable insight in the behaviour of the algorithms.

[1]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[2]  John J. Grefenstette.  Proceedings of the First International Conference on Genetic Algorithms and their Applications, July 24-26, 1985, at the Carnegie-Mellon University, Pittsburgh, PA , 1988 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  I. Morgenstern,et al.  Heidelberg Colloquium on Glassy Dynamics , 1987 .

[5]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[6]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[7]  Marcel J. E. Golay,et al.  The merit factor of long low autocorrelation binary sequences , 1982, IEEE Trans. Inf. Theory.

[8]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[9]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[10]  J. Bernasconi.  Low autocorrelation binary sequences : statistical mechanics and configuration space analysis , 1987 .

[11]  Lawrence Davis,et al.  Genetic Algorithms and Simulated Annealing , 1987 .

[12]  Karl Heinz Hoffmann,et al.  The optimal simulated annealing schedule for a simple model , 1990 .

[13]  R. Janßen,et al.  Minimizing spin-glass hamiltonians in APL2 , 1989, APLQ.

[14]  J. P. Secrétan,et al.  Der Saccus endolymphaticus bei Entzündungsprozessen , 1944 .

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .