一种基于自适应边界约束的高效遗传算法OA北大核心CSTPCD
An Efficient Genetic Algorithm Based on Adaptive Boundary Constraint
针对遗传算法中用于多父体重组的系数向量缺乏高效生成方法的问题,提出一种基于自适应边界约束(ABC)的高效遗传算法.该方法依据前一个系数的值,自适应缩放后一个系数的边界,可在任意多的父代重组情形下快速生成系数向量.在CEC2017标准数据集上的实验结果表明,所提算法在29个复杂优化问题上的表现都优于经验概率分布(EDBF)算法.
According to the lack of method for highly efficiently spawning coefficients for multi-parent recom-bination in real-encoded genetic algorithm,an efficient genetic algorithm based on adaptive boundary constraint (ABC) is proposed.This method quickly generates coefficient vectors by adaptively scaling the boundary of the subsequent coefficient based on the value of the previous one,allowing for efficient reconstitution under any num-ber of parent recombination scenarios.Experiment results on CEC2017 benchmarks demenstrate that proposed algorithm outperforms EDBF (empirical distribution based framework) a lot in 29 optimization problems.
黄铭;王龙波;肖明虹;傅毓;左正康
广西壮族自治区地理信息测绘院,柳州 545005广西壮族自治区自然资源厅,南宁 530022太原理工大学矿业工程学院,太原 030024
最优化理论遗传算法系数向量收敛效率经验概率分布(EDBF)自适应边界约束(ABC)
optimization theorygenetic algorithmcoefficient vectorconvergence efficiencyempirical distribution based framework (EDBF)adaptive boundary constraint (ABC)
《北京大学学报（自然科学版）》 2024 (004)
665-672 / 8
太原理工大学引进人才科研启动经费(RY2400000591)资助
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