scholarly journals Reflected Adaptive Differential Evolution with Two External Archives for Large-Scale Global Optimization

2016 ◽  
Vol 7 (2) ◽  
Author(s):  
Rashida Adeeb ◽  
Nasser Tairan ◽  
Muhammad Asif ◽  
Wali Khan ◽  
Abdel Salhi
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Large Scale ◽  
Adaptive Differential Evolution

scholarly journals Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Rashida Adeeb Khanum ◽  
Muhammad Asif Jan ◽  
Nasser Mansoor Tairan ◽  
Wali Khan Mashwani
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Test Problems ◽  
Adaptive Differential Evolution ◽  
External Archive ◽  
Search Capability

Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.

scholarly journals Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Abdulaziz S. Almazyad
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
High Dimensional ◽  
Gradual Change ◽  
Comparison Results ◽  
Mutation Strategy ◽  
Common Trade

This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

P-lsGOF: A parallel learning-selection-based global optimization framework

2020 ◽  
Vol 39 (5) ◽  
pp. 7333-7361
Author(s):  
Mingcheng Zuo ◽  
Guangming Dai
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Principal Component ◽  
Parallel Learning ◽  
Optimization Framework ◽  
Engineering Design Problems ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

When optimizing complicated engineering design problems, the search spaces are usually extremely nonlinear, leading to the great difficulty of finding optima. To deal with this challenge, this paper introduces a parallel learning-selection-based global optimization framework (P-lsGOF), which can divide the global search space to numbers of sub-spaces along the variables learned from the principal component analysis. The core search algorithm, named memory-based adaptive differential evolution algorithm (MADE), is parallel implemented in all sub-spaces. MADE is an adaptive differential evolution algorithm with the selective memory supplement and shielding of successful control parameters. The efficiency of MADE on CEC2017 unconstrained problems and CEC2011 real-world problems is illustrated by comparing with recently published state-of-the-art variants of success-history based adaptative differential evolution algorithm with linear population size reduction (L-SHADE) The performance of P-lsGOF on CEC2011 problems shows that the optimized results by individually conducting MADE can be further improved.

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