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A Novel Double-Strand DNA Genetic Algorithm for Multi-Objective Optimization
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DOI：10.1109/ACCESS.2019.2894726.png)
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摘要
En 中文
Multi-objective optimization is important for many businesses, science, and engineering applications. Existing evolutionary algorithms for multi-objective optimization problems based on single chain encoding still have difficulties in obtaining high-quality results. This paper presents a new DNA genetic algorithm that uses a novel double-strand DNA encoding, a set of new genetic operators, and two new ranking criteria to obtain solutions that closely approximate the Pareto-optimal front. The extensive experiments were performed using a set of comprehensive benchmark bi-objective and tri-objective test problems. The experimental results show that this algorithm outperforms a set of the state-of-the-art evolutionary algorithms on several well-accepted performance metrics.
Keyword:
Multi-objective optimization
DNA genetic algorithm
variant crowding distance
double strands
non-dominated sorting
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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