Improved Regularity Model-Based EDA for Many-Objective Optimization
By Yanan Sun, Gary G. Yen, Zhang Yi. 2018
43 citations recorded by the source corpus.
The performance of multiobjective evolutionary algorithms deteriorates appreciably in solving many-objective optimization problems (MaOPs) which encompass more than three objectives. One of the known rationales is the loss of selection pressure which leads to the selected parents not generating promising offspring toward Pareto-optimal front (PF) with diversity. Estimation of distribution algorithms sample new solutions with a probabilistic model built from the statistics extracting over the existing solutions so as to mitigate the adverse impact of genetic operators. In this paper, an improved regularity-based estimation of distribution algorithm is proposed to effectively tackle unconstrained MaOPs. In the proposed algorithm, diversity repairing mechanism is utilized to mend the areas, where need nondominated solutions with a closer proximity to the PF. Then favorable solutions are generated by the model built from the regularity of the solutions surrounding a group of representatives. These two steps collectively enhance the selection pressure which gives rise to the superior convergence of the proposed algorithm. In addition, dimension reduction technique is employed in the decision space to speed up the estimation search of the proposed algorithm. Finally, by assigning the Pareto-optimal solutions to the uniformly distributed reference vectors, a set of solutions with excellent diversity and convergence is obtained. To measure the performance, NSGA-III, GrEA, MOEA/D, HypE, MBN-EDA, and RM-MEDA are selected to perform comparison experiments over DTLZ and DTLZ− test suites with 3-, 5-, 8-, 10-, and 15-objective. Experimental results quantified by the selected performance metrics reveal that the proposed algorithm shows considerable competitiveness in addressing unconstrained MaOPs.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Numerical Analysis
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Citations in the corpus
- Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, background): “lems [19]–[21], mainly because of its gradient free and insensitiveness to local minimum [22]–[24].”
- Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (2018, background): “…include genetic algorithms (GAs) [23], genetic programming [24], evolutionary strategy [25], etc., among which GAs are the most popular one mainly because of their theoretical evidences [26] and promising performance in solving different optimization problems [27], [28], [29], [30], [31].”
- Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (2018, background): “…evolutionary algorithms include genetic algorithms (GAs) [23], genetic programming [24], evolutionary strategy [25], etc., among which GAs are the most popular one mainly because of their theoretical evidences [26] and promising performance in solving different optimization problems [27]–[31].”
- Completely Automated CNN Architecture Design Based on Blocks (2020, background): “…adopting the best ones as the parents could easily cause the loss of diversity in the population, which in turn leads to the premature convergence [44], [45], and as a result, the best performance of the population cannot be achieved [46], [47] due to trapping into the local minima [48], [49].”
- A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019, methodology): “, MaOEDA-IR) [11], and the reference vector guided EA (RVEA) [12].”