Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient
نویسندگان
چکیده
Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance a differential depends highly on its mutation and crossover strategy associated control parameters. However, determination process suitable parameter setting troublesome time consuming. Adaptive methods that can adapt to problem landscape environment are more preferable than fixed settings. This article proposes novel adaptive approach based learning from experiences over set In approach, modeled as finite-horizon Markov decision process. A reinforcement algorithm, named policy gradient, applied learn an agent (i.e., controller) provide parameters proposed adaptively during search procedure. learned compared against nine well-known evolutionary algorithms CEC'13 CEC'17 test suites. Experimental results show performs competitively these
منابع مشابه
Well Placement Optimization Using Differential Evolution Algorithm
Determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. It is a computationally intensive task due to the large number of simulation runs required. Therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum numbe...
متن کاملDeveloping Adaptive Differential Evolution as a New Evolutionary Algorithm, Application in Optimization of Chemical Processes
متن کامل
Adaptive Learning Differential Evolution for Numeric Optimization
Differential Evolution algorithm is a simple yet reliable and robust evolutionary algorithm for numeric optimization. However, fine-tuning control parameters of DE algorithm is a tedious and time-consuming task thus became a major challenge for its application. This paper introduces a novel self-adaptive method for tuning the amplification parameters F of DE dynamically. This method sampled app...
متن کاملwell placement optimization using differential evolution algorithm
determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. it is a computationally intensive task due to the large number of simulation runs required. therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum number of...
متن کاملAn Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challeng...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Evolutionary Computation
سال: 2021
ISSN: ['1941-0026', '1089-778X']
DOI: https://doi.org/10.1109/tevc.2021.3060811