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Rank-density-based multiobjective genetic algorithm and benchmark test function study

机译:基于秩密度的多目标遗传算法和基准测试函数研究

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Concerns the use of evolutionary algorithms (EA) in solving multiobjective optimization problems (MOP). We propose the use of a rank-density-based genetic algorithm (RDGA) that synergistically integrates selected features from existing algorithms in a unique way. A new ranking method, automatic accumulated ranking strategy, and a "forbidden region" concept are introduced, completed by a revised adaptive cell density evaluation scheme and a rank-density-based fitness assignment technique. In addition, four types of MOP features, such as discontinuous and concave Pareto front, local optimality, high-dimensional decision space and high-dimensional objective space are exploited and the corresponding MOP test functions are designed. By examining the selected performance indicators, RDGA is found to be statistically competitive with four state-of-the-art algorithms in terms of keeping the diversity of the individuals along the tradeoff surface, tending to extend the Pareto front to new areas and finding a well-approximated Pareto optimal front.
机译:关注在解决多目标优化问题(MOP)中使用进化算法(EA)。我们建议使用基于秩密度的遗传算法(RDGA),以独特的方式协同集成现有算法中的选定特征。引入了一种新的排名方法,自动累积排名策略和“禁区”概念,并通过修改后的自适应小区密度评估方案和基于排名密度的适应度分配技术来完成。此外,还利用了不连续和凹面的Pareto前沿,局部最优,高维决策空间和高维目标空间等四种类型的MOP特征,并设计了相应的MOP测试功能。通过检查选定的绩效指标,发现RDGA在四个方面的算法上在统计上都具有竞争力,可以保持个体在权衡面上的多样性,倾向于将Pareto前沿扩展到新的领域并找到一个近似的帕累托最优前沿。

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