基于期望生存率的动态自适应粒子群算法

针对惯性权重线性递减粒子群算法(LDPSO)不能适应复杂的非线性优化搜索过程的问题,提出了一种动态改变惯性权重的自适应粒子群算法(DAPSO).在该算法中引入期望生存率的概念,并根据它对粒子群算法搜索能力的影响,将惯性因子表示为期望生存率的函数.每次迭代时算法可根据当前粒子群平均期望生存率的大小动态地改变惯性权重,从而使算法具有动态自适应性.对6个典型函数的测试结果表明,DAPSO算法的收敛速度明显优于LDPSO算法,收敛精度也有所提高.

Dynamical Adaptive Particle Swarm Algorithm Based on Expected Alive Rate

A new adaptive Particle Swarm Optimization algorithm with dynamically changing inertia weight (DAPSO) is presented to solve the problem that the linearly decreasing weight (LDPSO) of the Particle Swarm Optimization algorithm cannot adapt to the complex and nonlinear optimization process. The expected alive rare of the particle swarm is introduced in this new algorithm and the weight is formulated as a function of this factor according to its impact on the search performance of the swarm. In each iteration process, the weight is changed dynamically based on the current average expected alive rate value, which provides the algorithm with effective dynamic adaptability. The algorithm of LDPSO and DAPSO are tested with six well-known benchmark functions. The experiments show that the convergence speed of DAPSO is significantly superior to LDPSO, and the convergence accuracy is increased.