植物繁殖算法及NSGA-II在多目标线性规划中的应用

Paschal Bisong Nyiam, A. Salhi
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引用次数: 0

摘要

多目标线性规划(MOLP)问题通常用精确的方法求解。然而,受自然启发的基于种群的随机算法,如植物繁殖算法,正变得越来越突出。本文首次将多目标植物繁殖算法(MOPPA)和非支配排序遗传算法II (NSGA-II)应用于MOLP,并将其结果与著名的精确方法进行了比较。现有51个MOLP实例的计算结果表明,MOPPA优于四种最突出的精确方法,即扩展多目标单纯形算法(EMSA)、仿射尺度内部MOLP算法(ASIMOLP)、Benson的外部近似算法(BOA)和参数单纯形算法(PSA)。
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Application of the Plant Propagation Algorithm and NSGA-II to Multiple Objective Linear Programming
: Multiple Objective Linear Programming (MOLP) problems are usually solved by exact methods. However, nature-inspired population based stochastic algorithms such as the plant propagation algorithm are becoming more and more prominent. This paper applies the multiple objective plant propagation algorithm (MOPPA) and nondominated sorting genetic algorithm II (NSGA-II) for the first time to MOLP and compares their outcomes with those of prominent exact methods. Computational results from a collection of 51 existing MOLP instances suggests that MOPPA compares favourably with four of the most prominent exact methods namely extended multiple objective simplex algorithm (EMSA), affine scaling interior MOLP algorithm (ASIMOLP), Benson’s outer-approximation algorithm (BOA) and parametric simplex algorithm (PSA)
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CiteScore
3.10
自引率
4.00%
发文量
77
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