Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization

Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a...

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Veröffentlicht in:Processes 2019-06, Vol.7 (6), p.362
Hauptverfasser: Khanum, Rashida, Jan, Muhammad, Tairan, Nasser, Mashwani, Wali, Sulaiman, Muhammad, Khan, Hidayat, Shah, Habib
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container_end_page
container_issue 6
container_start_page 362
container_title Processes
container_volume 7
creator Khanum, Rashida
Jan, Muhammad
Tairan, Nasser
Mashwani, Wali
Sulaiman, Muhammad
Khan, Hidayat
Shah, Habib
description Differential Evolution (DE) is one of the prevailing search techniques in the present era to solve global optimization problems. However, it shows weakness in performing a localized search, since it is based on mutation strategies that take large steps while searching a local area. Thus, DE is not a good option for solving local optimization problems. On the other hand, there are traditional local search (LS) methods, such as Steepest Decent and Davidon–Fletcher–Powell (DFP) that are good at local searching, but poor in searching global regions. Hence, motivated by the short comings of existing search techniques, we propose a hybrid algorithm of a DE version, reflected adaptive differential evolution with two external archives (RJADE/TA) with DFP to benefit from both search techniques and to alleviate their search disadvantages. In the novel hybrid design, the initial population is explored by global optimizer, RJADE/TA, and then a few comparatively best solutions are shifted to the archive and refined there by DFP. Thus, both kinds of searches, global and local, are incorporated alternatively. Furthermore, a population minimization approach is also proposed. At each call of DFP, the population is decreased. The algorithm starts with a maximum population and ends up with a minimum. The proposed technique was tested on a test suite of 28 complex functions selected from literature to evaluate its merit. The results achieved demonstrate that DE complemented with LS can further enhance the performance of RJADE/TA.
doi_str_mv 10.3390/pr7060362
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source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; MDPI - Multidisciplinary Digital Publishing Institute
subjects Algorithms
Archives & records
Evolution
Evolutionary computation
Global optimization
Local optimization
Mutation
Optimization
Population
Searching
title Global Evolution Commended by Localized Search for Unconstrained Single Objective Optimization
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