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Memory-based CHC algorithms for the dynamic traveling salesman problem

The CHC algorithm uses an elitist selection method that, combined with an incest prevention mechanism and a method to diverge the population whenever it converges, allows the maintenance of the population diversity. This algorithm was successfully used in the past for static optimization problems. The use of memory in Evolutionary Algorithms has been proved to be advantageous when dealing with dynamic optimization problems. In this paper we investigate the use of three different explicit memory strategies included in the CHC algorithm. These strategies - direct, immigrant and associative - combined with the CHC algorithm are used to solve different instances of the dynamic Traveling Salesman Problem in cyclic, noisy and random environments. The experimental results, statistically validated, show that the memory schemes significantly improve the performance of the original CHC algorithm for all types of studied environments. Moreover, when compared with the equivalent memory-based standard EAs with the same memory schemes, the memory-based CHC algorithms obtain superior results when the environmental changes are slower.