دانلود مقاله ISI انگلیسی شماره 78803
عنوان فارسی مقاله

اپراتور ترکیبی متقاطع انطباقی جدید برای الگوریتم تکاملی چند هدفه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
78803 2016 22 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Sciences, Volume 345, 1 June 2016, Pages 177–198

کلمات کلیدی
الگوریتم تکاملی؛ بهینه سازی چند هدفه؛ متقاطع باینری شبیه سازی شده - تکامل تفاضلی
پیش نمایش مقاله
پیش نمایش مقاله اپراتور ترکیبی متقاطع انطباقی جدید برای الگوریتم تکاملی چند هدفه

چکیده انگلیسی

In this paper, a novel recombination operator, called adaptive hybrid crossover operator (AHX), is designed for tackling continuous multiobjective optimization problems (MOPs), which works effectively to enhance the search capability of multiobjective evolutionary algorithms (MOEAs). Different from the existing hybrid operators that are commonly operated on chromosome level, the proposed operator is executed on gene level to combine the advantages of simulated binary crossover (SBX) with local search ability and differential evolution (DE) with strong global search capability. More opportunities are assigned to DE in the early evolutionary stage for gene-level global search in decision space; whereas, with the generation grows, more chances are gradually allocated to SBX for gene-level local search. The balance between the gene-level global and local search is well maintained by an adaptive control approach in AHX. To validate the effectiveness of AHX, it is studied by substituting the original recombination operators in the four state-of-the-art MOEAs (i.e., NSGA-II, SPEA2, SMS-EMOA, and MOEA/D), and the performance of revised algorithms is significantly improved. Furthermore, AHX is also compared to three recently proposed recombination operators, such as a newly DE inspired (DEI) recombination operator, a learning paradigm based on jumping genes (JGBL) and a bandit-based adaptive operator selection approach (FRRMAB). The experimental studies validate that AHX can be effectively integrated into different frameworks of MOEAs, and performs better than SBX, DE, DEI, JGBL and FRRMAB in solving various kinds of MOPs.

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پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.