摘要
为了求解离散空间中的最优化问题,提出了一种二进制蝙蝠算法,并引入时变惯性因子来提高算法的全局收敛速度;在此基础上,为提高求解0-1背包问题时找到最优解的机率,利用贪心优化策略对无效的蝙蝠个体进行优化,从而给出了贪心二进制蝙蝠算法(GBBA)。仿真计算结果表明,GBBA算法在寻优能力和收敛性能方面比已有的GMBA算法都更优越。
For solving the optimization problem in discrete space, a Binary Bat Algorithm (BBA) is proposed, and time-varying inertia factor is introduced to improve the global convergence speed of the algorithm. In order to increase the probability of finding the optimal solution in solving 0-1 knapsack problem, greedy strategy is used in the algorithm, thus a Greedy Binary Bat Algorithm (GBBA) is proposed. Simulations show that the proposed algorithm is much superior to GMBA algorithm in searching capability and convergence performance.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第19期71-74,79,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.11271257)
河北省高等学校科技基金(No.Z2013110)