Multi-particle Simulated Annealing
Whereas genetic algorithms and evolutionary methods involve a population of points, simulated annealing (SA) can be interpreted as a random walk of a single point inside a feasible set. The sequence of locations visited by SA is a consequence of the Markov Chain Monte Carlo sampler. Instead of running SA with multiple independent runs, in this chapter we study a multi-particle version of simulated annealing in which the population of points interact with each other. We present numerical results that demonstrate the benefits of these interactions on algorithm performance.
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