基于局部远亲差分增强的扰动粒子群优化算法

针对粒子群优化（PSO）算法在搜索过程中因个体间缺乏交互，使种群逐渐丧失多样性、导致算法陷入局部极值的问题，提出了一种基于局部远亲差分增强的扰动粒子群优化算法（LFDE-PPSO）。首先，为扩大种群搜索空间，在速度更新过程中引入扰动因子，使惯性权重、学习因子在小范围内波动；其次，引入重构概率，选择适应度值低的个体重建中间种群；最后，为增加种群多样性，使较差个体的优秀基因得以保留，引入粒子不相关性及远亲个体，利用不相关性选择与差分个体基因差异性较大的远亲进行差分增强。实验结果表明，所提算法能够使中间种群中适应度值高的个体得以保留，有效增加种群多样性，使种群具备较强的跳脱局部极值能力，加快粒子逼近全局最优，同时具有收敛快、精度高等优点。

Perturbation particle swarm optimization algorithm based on local far-neighbor differential enhancement

To solve the problems that Particle Swarm Optimization (PSO) algorithm is easy to fall into the local extremum due to the lack of interaction between individuals in the search process, the diversity of the population is gradually lost, a Perturbation Particle Swarm Optimization algorithm based on Local Far-neighbor Differential Enhancement (LFDE-PPSO) was proposed. Firstly, in order to enlarge the population search space, the disturbance factor was introduced to make inertia weight and learning factor fluctuate within a small range. Secondly, the reconstruction probability was introduced, and the population with low fitness value was selected to reconstruct intermediate population. Finally, in order to increase the population diversity, the excellent individuals of poor individuals were retained, the irrelevant and far-neighbor individuals were introduced. The far-neighbors with large differences from differential individual genes were used for differential enhancement. The experimental results show that the proposed algorithm can preserve individuals with high fitness in the intermediate population, effectively increase the population diversity, make the population have strong ability to jump out of local extremum, speed up the particle approximation to the global aptimum, and have the advantages of fast convergence and high precision.