科研之友
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摘要
Inspired by the intelligent foraging behavior of honey bees, the artificial bee colony algorithm (ABC), a swarm-based stochastic optimization method, has shown to be very effective and efficient for solving optimization problems. However, since its solution search equation is good at exploration but poor at exploitation, ABC often suffers from a slow convergence speed. To better balance the tradeoff between exploration and exploitation, in this paper, we propose a depth-first search (DFS) framework. The key feature of the DFS framework is to allocate more computing resources to the food sources with better quality and easier to be improved for evolution. We apply the DFS framework to ABC, GABC and CABC, yielding DFSABC, DFSGABC and DFSCABC respectively. The experimental results on 22 benchmark functions show that the DFS framework can speed up convergence rate in most cases. To further improve the performance, we introduce two novel solution search equations: the first equation incorporates the information of elite solutions and can be applied to the employed bee phase, while the second equation not only exploits the information of the elite solutions but also employs the current best solution in the onlooker bee phase. Finally, two novel proposed search equations are combined with DFSABC to form a new variant of ABC, named DFSABC_elite. Through the comparison of DFSABC_elite with other variants of ABC and some non-ABC methods, the experimental results demonstrate that DFSABC_elite is significantly better than the compared algorithms on most of the test functions in terms of solution quality, robustness, and convergence speed.
