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首页> 外文期刊>IEEE transactions on industrial informatics >Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data
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Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data

机译：基于非支配排序和双向局部大数据搜索的多目标进化算法

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The improved differential evolutionary algorithm (EA) discussed in this paper is used to solve high-dimensional big data. Specifically, the algorithm improves population diversity by expanding the searching scope of the population, prevents premature deaths of the population through wider and more specific searches, and aims to solve the high-dimensional issue. To achieve this improvement goal, the paper suggests a multilayer hierarchical architecture on the basis of the above-mentioned heuristic mechanism. In each layer of the hierarchical architecture in the dynamic subpopulation, individuals who are more suitable for isolated evolution can better coexist with the original main population. We propose a new multiobjective optimization algorithm based on nondominated sorting and bidirectional local search (NSBLS). The algorithm takes the local beam search as the main body. NSBLS outputs the nondominated solution set through a continuous iterative search when the iteration termination condition is satisfied. It is worthy to note that the iteration of NSBLS is similar to the generation of the EA; therefore, this paper uses generation to represent the iterations. An algorithm introduces a new distribution maintaining strategy based on the sampling theory to combine with the fast nondominated sorting algorithm in order to select a new population into the next iteration. NSBLS will compare with three classical algorithms: NSGA-II, MOEA/D-DE, and MODEA through a series of bi-objective test problems. The proposed nondominated sorting and local search is able to find a better spread of solutions and better convergence to the true Pareto-optimal front compared to the other four algorithms. The outstanding performance of the proposed technology was proven in well-known benchmark problems.
机译：本文讨论的改进的差分进化算法（EA）用于求解高维大数据。具体而言，该算法通过扩大人口搜索范围来改善人口多样性，通过更广泛，更具体的搜索来防止人口过早死亡，并旨在解决高维问题。为了实现这一改进目标，本文提出了一种基于上述启发式机制的多层层次结构。在动态子种群的层次结构的每一层中，更适合于独立进化的个体可以更好地与原始主要种群共存。我们提出了一种新的基于非支配排序和双向局部搜索（NSBLS）的多目标优化算法。该算法以局部波束搜索为主体。当满足迭代终止条件时，NSBLS通过连续迭代搜索输出非支配解集。值得注意的是，NSBLS的迭代类似于EA的生成。因此，本文使用生成来表示迭代。一种算法引入了一种新的基于采样理论的分布维护策略，并与快速的非支配排序算法相结合，以便在下一次迭代中选择新的种群。 NSBLS将通过一系列双目标测试问题与三种经典算法进行比较：NSGA-II，MOEA / D-DE和MODEA。与其他四种算法相比，拟议的非支配排序和局部搜索能够找到更好的解决方案分布，并且能够更好地收敛到真正的帕累托最优前沿。所提出技术的出色性能已在众所周知的基准问题中得到证明。

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