基于目标空间分解和连续变异的多目标粒子群算法

针对当前多目标粒子群优化算法收敛性和多样性不佳等问题，提出了一种基于目标空间分解和连续变异的多目标粒子群优化算法。利用目标空间分解方法将粒子群分配到预先设定好的子区域中，在该过程中，通过一种新适应值公式来对每个子区域中的粒子进行择优筛选，该适应值公式融入了支配强度因素；在全局搜索过程中，使用差分变异、高斯变异和柯西变异对全局引导粒子的位置进行连续变异操作。将该算法与当前主流的一些多目标优化算法进行对比实验，结果表明，本文提出的算法在提高粒子收敛性的同时，多样性也得到了提升。

Decomposition and continuous mutation-based multi-objective particle swarm optimization

In light of the poor convergence problems and the diversity of current multi-objective optimization algorithms, in this paper, we propose an objective-space decomposition and continuous mutation-based multi-objective particle-swarm-optimization algorithm. Its innovations are as follows:we use a space decomposition method to distribute the particle swarm into a predefined sub-region. During this process, we apply a new adaptive value formula to select and filter the particles in each sub-region and incorporate a fitness formula into the dominance factor. In the global search process, we apply differential, Gaussian, and Cauchy mutations to continuously mutate the position of the global guide particle. We compare the performance of this algorithm with those of current multi-objective optimization algorithms, and the results show that the proposed algorithm improves the convergence and diversity of the particles.