基于随机黑洞和自适应策略的引力搜索算法

针对引力搜索算法过于随机、全局搜索能力差、容易陷入局部最优等问题，提出一种基于随机黑洞策略和自适应策略的改进引力搜索算法。引入比例系数n来自适应调节引力常量，使算法在运算初期加大搜索力度，有效避免算法陷入局部最优。将随机黑洞理论中，粒子在黑洞附近容易被黑洞捕获的现象引入算法中，即在黑洞半径内的粒子将会被黑洞捕获，但又有一定的概率逃逸。这样既提高了局部搜索能力，又不失全局搜索能力，且提高了收敛速度。与标准的搜索算法相比，改进后的引力搜索算法具有较快的收敛特性和较好的优化性能。

Gravitational Search Algorithm Based on Random Black Hole and Adaptive Strategy

An improved gravitational search algorithm based on random black hole strategy and gravitational constant adaptive strategy is proposed to solve the problem that the gravitational search algorithm is too random, the global search ability is poor, and the local optimum is easy to fall into. The proportion coefficient n is introduced from the adaptive adjustment of gravity constant, which makes the algorithm increase the search power in the initial stage, and effectively avoid the algorithm falling into local optimum. As for random black hole theory, the phenomenon that particles are easily captured by black hole near the black hole. This was considered for the algorithm. The particles in the radius of the black hole would be captured by the black hole, but there would be a certain probability to escape, which not only improves the local search ability, but also improves the global search ability and the convergence speed. Compared with the cable algorithm, the improved gravitational search algorithm has faster convergence and better optimization performance.