Improving the search performance of SHADE using linear population size reduction

Tanabe, Ryoji; Fukunaga, Alex

doi:10.1109/cec.2014.6900380

Cited by 989 publications

(583 citation statements)

References 24 publications

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“…Following the methodology described in Tanabe & Fukunaga (2014) we used the Wilcoxon rank-sum test with significance level p < 0.05 to compare the evaluation (Tanabe & Fukunaga, 2014), 3. United Multi-Operator Evolutionary Algorithms (UMOEAs) (Elsayed et al, 2014).…”

Section: Methods Testing On Benchmark Functionsmentioning

confidence: 99%

A software for parameter optimization with Differential Evolution Entirely Parallel method

Kozlov

Samsonov

Samsonova

2016

PeerJ Computer Science

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Summary. Differential Evolution Entirely Parallel (DEEP) package is a software for finding unknown real and integer parameters in dynamical models of biological processes by minimizing one or even several objective functions that measure the deviation of model solution from data. Numerical solutions provided by the most efficient global optimization methods are often problem-specific and cannot be easily adapted to other tasks. In contrast, DEEP allows a user to describe both mathematical model and objective function in any programming language, such as R, Octave or Python and others. Being implemented in C, DEEP demonstrates as good performance as the top three methods from CEC-2014 (Competition on evolutionary computation) benchmark and was successfully applied to several biological problems. Availability. DEEP method is an open source and free software distributed under the terms of GPL licence version 3. The sources are available at http://deepmethod. sourceforge.net/ and binary packages for Fedora GNU/Linux are provided for RPM package manager at https://build.opensuse.org/project/repositories/home:mackoel: compbio.

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Section: Methods Testing On Benchmark Functionsmentioning

confidence: 99%

A software for parameter optimization with Differential Evolution Entirely Parallel method

Kozlov

Samsonov

Samsonova

2016

PeerJ Computer Science

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“…CoDE [35] combines three well-studied trial vector generation strategies with three random control parameter settings to generate trial vectors. In L-SHADE [36], the Linear Population Size Reduction (LPSR) is embedded into SHADE so that the robustness of the algorithm is improved. Swagatam [37] proposed an improvement mechanism of DE by using the concept of the neighborhood of each population member.…”

Section: Generation Strategy Of Differential Evolutionmentioning

confidence: 99%

“…(1) Test Problems and Dimension Setting: For a comprehensive evaluation of MCPDE, all the CEC2013 [36] benchmark functions are used to evaluate the performance of MCPDE. The CEC2013 benchmark set consists of 28 test functions.…”

Section: General Experimental Settingmentioning

confidence: 99%

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

2016

Algorithms

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Abstract:The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems.

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“…For convenient description, these functions [19] are denoted as F1-F15, as shown in Table 1. The SPS-L-SHADE-EIG algorithm combines the adaptive differential evolution [20,21] with linear population size reduction(L-SHADE) [22] with the eigenvector-based (EIG) [23] crossover and successful-parent-selecting (SPS) frameworks [24]. The DEsPA algorithm is a new Differential Evolution algorithm with a success-based parameter adaptation with resizing population space [25].…”

Section: Cec 2015 Benchmarksmentioning

confidence: 99%

Improved Biogeography-Based Optimization Based on Affinity Propagation

Wang

Liu

Ren

et al. 2016

IJGI

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Abstract:To improve the search ability of biogeography-based optimization (BBO), this work proposed an improved biogeography-based optimization based on Affinity Propagation. We introduced the Memetic framework to the BBO algorithm, and used the simulated annealing algorithm as the local search strategy. MBBO enhanced the exploration with the Affinity Propagation strategy to improve the transfer operation of the BBO algorithm. In this work, the MBBO algorithm was applied to IEEE Congress on Evolutionary Computation (CEC) 2015 benchmarks optimization problems to conduct analytic comparison with the first three winners of the CEC 2015 competition. The results show that the MBBO algorithm enhances the exploration, exploitation, convergence speed and solution accuracy and can emerge as the best solution-providing algorithm among the competing algorithms.

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Improving the search performance of SHADE using linear population size reduction

Cited by 989 publications

References 24 publications

A software for parameter optimization with Differential Evolution Entirely Parallel method

A software for parameter optimization with Differential Evolution Entirely Parallel method

A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization

Improved Biogeography-Based Optimization Based on Affinity Propagation

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