Abstract
Recently, a new algorithm named dynamic group optimization (DGO) has been proposed, which lends itself strongly to exploration and exploitation. Although DGO has demonstrated its efficacy in comparison to other classical optimization algorithms, DGO has two computational drawbacks. The first one is related to the two mutation operators of DGO, where they may decrease the diversity of the population, limiting the search ability. The second one is the homogeneity of the updated population information which is selected only from the companions in the same group. It may result in premature convergence and deteriorate the mutation operators. In order to deal with these two problems in this paper, a new hybridized algorithm is proposed, which combines the dynamic group optimization algorithm with the cross entropy method. The cross entropy method takes advantage of sampling the problem space by generating candidate solutions using the distribution, then it updates the distribution based on the better candidate solution discovered. The cross entropy operator does not only enlarge the promising search area, but it also guarantees that the new solution is taken from all the surrounding useful information into consideration. The proposed algorithm is tested on 23 up-to-date benchmark functions; the experimental results verify that the proposed algorithm over the other contemporary population-based swarming algorithms is more effective and efficient.
Highlights
The increasing complexity in modern engineering designs and systems, demands for advances in search algorithms for optimization and analysis [1]
Assume D is the dimensionality of the search space, the MaxFEs is the maximum number of function evaluation number, and the Cof is the cost of the objective function
The results prove that cross entropy dynamic group optimization (CEDGO) algorithm can achieve satisfactory results on those testing functions when compared with these evolutionary algorithm (EA)
Summary
The increasing complexity in modern engineering designs and systems, demands for advances in search algorithms for optimization and analysis [1]. In the intragroup cooperation action, members evolve based on the information of the heads and the global best solution obtained so far, so it belongs to EA. The most distinctive improvement by DGO over the other algorithms is that the other algorithms are influenced by either a global optimum or its own best solution previously obtained so far They lack of cooperation amongst groups, and different groups never share information. It uses two classical mutation operators to exploit the local area and accelerate the convergence All those advantages shows that DGO is a promising optimization tool. To improve the efficiency for finding the best solution and avoiding the local optima, a new hybrid meta-heuristic algorithm called the cross entropy dynamic group optimization (CEDGO).
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