基于粒子群算法的不确定动态多目标优化方法研究

当前不确定动态多目标优化方法通常将多目标问题转换成单目标问题,将其它目标看作约束条件,仅可得到单个解,无法有效体现不确定多目标之间的关系,导致得到的解质量低。为此,提出一种新的基于粒子群算法的不确定动态多目标优化方法,给出不确定动态多目标优化问题的数学描述,介绍了粒子群算法,针对粒子群算法容易陷入局部最优的弊端,引入动态变异算子对其进行改进,通过改进的位置更新公式实现粒子群算法位置的自适应更新,给出解决不确定多目标优化问题的详细过程,在此基础上,通过分段线性函数参数化实现不确定动态多目标优化。实验结果表明,所提方法搜索能力强,采用所提方法得到的解与真实解最相近,质量最高。

Based on particle swarm algorithm of uncertain dynamic multi-objective optimization method research

the current uncertain dynamic multiobjective optimization method is usually the multi-objective problem into a single objective problem, see other goals as constraint conditions, and only a single solution can be obtained, cannot effectively reflect the relationship between the uncertain multi-objective, cause for the low quality of solution.For this, based on particle swarm algorithm is a new kind of uncertain dynamic multi-objective optimization method, uncertain dynamic multi-objective optimization problem, this paper introduces the particle swarm optimization (pso) algorithm, aiming at the disadvantages of the particle swarm algorithm easy to fall into local optimum, the introduction of dynamic mutation operator to improve, through improving the position of the updating formula to realize adaptive update position of particle swarm optimization (pso), is given in detail to solve the problem of uncertain multi-objective optimization process, on this basis, through the piecewise linear function parametric uncertain dynamic multi-objective optimization.Experimental results show that the proposed method search ability, using the proposed method is close to the real solution of the solution, the highest quality.