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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Enhanced theta dominance and density selection based evolutionary algorithm for many-objective optimization problems
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Enhanced theta dominance and density selection based evolutionary algorithm for many-objective optimization problems

机译：基于多目标优化问题的基于θ优势和密度选择的θ优势和密度选择

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摘要

Many multi-objective evolutionary algorithms (MOEAs) have been developed for many-objective optimization. This paper proposes a new enhanced oee integral dominance and density selection based evolutionary algorithm (called oee integral-EDEA) for many-objective optimization problems. We firstly construct an m-dimension hyper-plane using the extreme point on the each dimension. Then we replace the distance between the origin point and projection of solution on the reference line of oee integral dominance which recently is proposed in oee integral dominance based evolutionary algorithm (oee integral-DEA), with the perpendicular distance between each solution and the hyper-plane to develop an enhanced oee integral dominance. Finally, in order to maintain better diversity, oee integral-EDEA employs density based selection mechanism to select the solution for the next population in the environment selection phase. oee integral-EDEA still inherits clustering operator and ranking operator of oee integral-DEA to balance diversity and convergence. The performance of oee integral-EDEA is validated and compared with five state-of-the-art algorithms on two well-known many-objective benchmark problems with three to fifteen objectives. The results show that oee integral-EDEA is capable of obtaining a solution set with better convergence and diversity.
机译：许多多目标进化算法（MOEAS）已经开发用于多目标优化。本文提出了一种新的增强的OEE积分优势和基于浓度选择的进化算法（称为OEE Integral-EDEA），用于多目标优化问题。我们首先使用每个维度上的极端点构建一个M尺寸超平面。然后，我们更换在oee积分优势的参考线上的原点点和解投影之间的距离，最近被基于Oee积分优势的进化算法（OEE积分DEA），每个解决方案和超 - 的垂直距离飞机发展增强的OEE积分优势。最后，为了保持更好的多样性，OEE Integral-EDEA采用基于密度的选择机制来选择环境选择阶段的下一个群体的解决方案。 OEE Integral-Edea仍然继承了OEE Integral-DEA的群集运算符和排名运算符，以平衡多样性和收敛性。 OEE积分 - EDEA的性能被验证，并与五个最先进的算法进行了比较，两个众所周知的许多客观基准问题有三到十五个目标。结果表明，OEE积分 - EDEA能够获得具有更好收敛和多样性的解决方案。

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