Multiobjective Evolutionary Algorithm Based on Nondominated Sorting and Bidirectional Local Search for Big Data

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.