关键词: 果蝇优化, 群体协同, 和声搜索, 函数优化

Abstract: To deal with the drawbacks of trapping in local optimal solutions easily and low convergence accuracy of the standard fruit fly optimization (FFO) algorithm, this paper proposes a novel fruit fly optimization algorithm by combining the swarm collaboration (SC) and harmony search (HS) strategies, named as FFO-SC+HS. In each iteration of FFO-SC+HS, the food source location of each fruit fly is generated based on a single dimension that is randomly    determined and dynamic search radius, and two candidate location vectors derived from the reconstructed location set by SC strategy are considered during the update process of fruit fly swarm location. Further, one candidate location is the best one of the reconstructed location set, and the other one is obtained by means of HS strategy. Extensive computational experiments and comparison analysis are conducted upon 10 benchmark functions to validate the effectiveness of FFO-SC+HS. As demonstrated in the results, FFO-SC+HS outperforms other 4 reported FFO algorithms in terms of solution quality and convergence efficiency. Moreover, it is found that different combinations of three main parameters have a significant impact on optimization performances of FFO-SC+HS, and both of the SC and HS strategies are     indispensable.

Key words: fruit fly optimization, swarm collaboration, harmony search, function optimization