An analysis of heterogeneous cooperative algorithms

Most optimization algorithms suffer from a significant deterioration in performance as the dimensionality and complexity of the problem search space increases. Also these algorithms, given certain configurations, typically show markedly improved performance on a particular problem only to exhibit poor performance on another. The first issue could be resolved by using a cooperative algorithm to divide the problem complexity among its participating algorithms, making the problem easier to solve. The second issue could then be resolved with the use of differently configured participating algorithms within the overall cooperative algorithm. This paper investigates the possibility of combining different population-based algorithms within a cooperative algorithm. The aim is to take advantage of different algorithm characteristics regarding parameter settings, explorative/exploitative capacity, convergence speed and other behaviors in finding solutions to various optimization problems.

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