School of Automation, China University of Geosciences, Wuhan 430074, China.
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
Comput Intell Neurosci. 2017;2017:6153951. doi: 10.1155/2017/6153951. Epub 2017 Nov 23.
This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.
本文提出了一种新的基于黑洞算法的多目标进化算法，该算法具有新的个体密度评估（细胞密度），称为“自适应多目标黑洞算法”（AMOBH）。细胞密度具有计算复杂度低的特点，并保持了帕累托前沿的收敛性和多样性之间的良好平衡。AMOBH 的框架可以分为三个步骤。首先，将帕累托前沿映射到一个称为平行细胞坐标系的新目标空间。然后，为了自适应地调整进化策略，使用香农熵来估计进化状态。最后，将细胞密度与称为细胞优势的优势强度评估相结合，以评估解决方案的适应性。与最先进的 SPEA-II、PESA-II、NSGA-II 和 MOEA/D 方法相比，实验结果表明，在大多数情况下，AMOBH 在收敛速度、种群多样性、种群收敛性、不同 Pareto 区域的子种群获取以及时间复杂度等方面都具有良好的性能。
