The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions.

差分进化（DE）算法的变异策略的选择在优化性能方面具有重要作用，例如探索能力，收敛精度和收敛速度。为了提高这些性能，本文开发了一种具有邻域变异算子和基于对立学习的改进的差分进化算法，即NBOLDE。在提出的NBOLDE中，将新的评估参数和权重因子引入邻域模型，以提出新的邻域策略。在此基础上，设计了一种基于DE / current-to-best / 1的新邻域突变策略，即DE / neighbor-to-neighbor / 1，以高搜索量取代局部全局突变。效率。然后，通过基于广义对立面的学习来优化初始种群，并在当前解和反向解之间选择更好的解，以近似全局最优解，从而可以修正收敛方向，加速收敛，提高效率，提高稳定性和避免过早收敛。最后，将拟议的NBOLDE与四个最新的DE变量通过12个具有低维和高维的基准函数进行比较。实验结果表明，所提出的NBOLDE算法在求解高维复杂函数时具有更快的收敛速度，更高的收敛精度和更好的优化能力。可以修改收敛方向，加快收敛速度​​，提高效率，增强稳定性，避免过早收敛。最后，将拟议的NBOLDE与四个最新的DE变体通过12个具有低维和高维的基准功能进行比较。实验结果表明，所提出的NBOLDE算法在求解高维复杂函数时具有更快的收敛速度，更高的收敛精度和更好的优化能力。可以修改收敛方向，加快收敛速度​​，提高效率，增强稳定性，避免过早收敛。最后，将拟议的NBOLDE与12个具有低维和高维功能的基准功能与四个最新的DE变体进行比较。实验结果表明，所提出的NBOLDE算法在求解高维复杂函数时具有更快的收敛速度，更高的收敛精度和更好的优化能力。