A novel approach is proposed in this paper to improve the optimization proficiency of the differential evolution (DE) algorithm in the presence of stochastic noise in the objective surface by utilizing the composite benefit of four strategies. The first strategy is devised with an aim to employ reinforcement learning scheme of stochastic learning automata for autonomous selection of the sample size of a trial solution (for its repeated fitness evaluation) based on the characteristics of the fitness landscape in its local neighborhood. The second stratagem is proposed to estimate the effective fitness measure from multiple fitness samples of a trial solution, resulting from sampling. The novelty of the second policy lies in considering the distribution of noisy samples during effective fitness evaluation, instead of their direct averaging. The third strategy deals with amelioration of the DE/current-to-best/1 mutation scheme to judiciously direct the search in promising region, even in prevailing existence of noise in the objective surface. Finally, the greedy selection policy of the traditional DE is modified by introducing the principle of probabilistic crowding induced niching to ensure both the population quality and the population diversity. Comparative analysis performed on simulation results for diverse noisy benchmark functions reveal the statistically significant superiority of the proposed algorithm to its contenders with respect to function error value.

本文提出了一种新颖的方法，通过利用四种策略的综合优势，在目标表面存在随机噪声的情况下提高差分演化（DE）算法的优化能力。设计第一个策略的目的是采用随机学习自动机的强化学习方案，以根据当地社区的健身景观特征自主选择试验解决方案的样本量（用于反复进行适应性评估）。提出第二种策略是从抽样得出的试验解决方案的多个适应度样本中估算有效适应度度量。第二种策略的新颖之处在于在有效的适应性评估过程中考虑噪声样本的分布，而不是直接进行平均。第三种策略是改善DE / current-to-best / 1突变方案，以明智地将搜索引导到有希望的区域，即使在目标表面存在噪声的情况下也是如此。最后，通过引入概率拥挤诱导小生境的原理，对传统DE的贪婪选择策略进行了修改，以确保种群质量和种群多样性。对各种噪声基准函数的仿真结果进行的比较分析表明，相对于函数误差值，该算法相对于其竞争者具有统计学上的显着优势。传统DE的贪婪选择策略通过引入概率拥挤诱导小生境的原则进行了修改，以确保种群质量和种群多样性。对各种噪声基准函数的仿真结果进行的比较分析表明，相对于函数误差值，该算法相对于其竞争者具有统计学上的显着优势。传统DE的贪婪选择策略通过引入概率拥挤诱导小生境的原理进行了修改，以确保种群质量和种群多样性。对各种噪声基准函数的仿真结果进行的比较分析表明，相对于函数误差值，该算法在竞争者方面具有统计学上的显着优势。