求解高维多模优化问题的正交小生境自适应差分演化算法

针对传统优化算法在求解高维多模态优化问题时存在收敛速度慢、求解精度低的问题，提出一种基于正交设计与小生境精英策略的自适应差分进化算法ONDE。首先利用正交表产生初始种群，然后采用小生境精英策略来产生小生境种群(NP)，并用小生境种群更新精英个体；接着应用拥挤裁剪避免种群陷入局部搜索，最后利用自适应差分变异算子改进了差分进化(DE)算法。通过对7个benchmark函数仿真验证，实验结果表明，算法在收敛速度、求解精度和稳定性方面都有较大优势。

Self-adaptive differential evolution algorithm based on orthogonal and niche elite for high-dimensional multi-modal optimization

Traditional Differential Evolution (DE) algorithm has shortcomings, such as being trapped into local optimum easily, low convergence speed and solution precision. An Orthogonal Niche Differential Evolution (ONDE) algorithm was proposed to resolve these problems. Firstly, the orthogonal table was used to generate initial population; secondly, the niche elite selection strategy was utilized to produce Niche Population (NP), and update Elite Population (EP) with niche population; thirdly, trapping into local search was prevented by crowded cutting; finally, differential evolution operator was improved by using self-adaptive mutation operators. Simulations on seven benchmark functions were used to test the proposed algorithm. The experimental results illustrate that ONDE algorithm has some advantages in convergence velocity, solution precision and stability.