An Adaptive Unified Differential Evolution Algorithm for Global Optimization

Qiang, Ji; Mitchell, Chad

Title: An Adaptive Unified Differential Evolution Algorithm for Global Optimization

Full Record
Other Related Research

Abstract

In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.

Authors:: Qiang, Ji; Mitchell, Chad

Publication Date:: Mon Nov 03 00:00:00 EST 2014

Research Org.:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:: Accelerator & Fusion Research Division

OSTI Identifier:: 1163660

Report Number(s):: LBNL-6853E
Journal ID: 1568-4946

DOE Contract Number:: DE-AC02-05CH11231

Resource Type:: Journal Article

Journal Name:: Applied Soft Computing

Additional Journal Information:: Journal Name: Applied Soft Computing

Country of Publication:: United States

Language:: English

Subject:: 43 PARTICLE ACCELERATORS; differential evolution, evolutionary optimization

Citation Formats


                    Qiang, Ji, and Mitchell, Chad. An Adaptive Unified Differential Evolution Algorithm for Global Optimization.  United States: N. p., 2014. 
        Web.

Copy to clipboard


                    Qiang, Ji, & Mitchell, Chad. An Adaptive Unified Differential Evolution Algorithm for Global Optimization.  United States.

Copy to clipboard


                    Qiang, Ji, and Mitchell, Chad. 2014.  
        "An Adaptive Unified Differential Evolution Algorithm for Global Optimization".  United States.  https://www.osti.gov/servlets/purl/1163660.

Copy to clipboard


                    
@article{osti_1163660,

  title        = {An Adaptive Unified Differential Evolution Algorithm for Global Optimization},

  author       = {Qiang, Ji and Mitchell, Chad},

  abstractNote = {In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.},

  doi          = {},

  url          = {https://www.osti.gov/biblio/1163660},
  journal      = {Applied Soft Computing},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Nov 03 00:00:00 EST 2014},

  month        = {Mon Nov 03 00:00:00 EST 2014}

}

Copy to clipboard

Journal Article:

View Journal Article (0.47 MB)

Other availability

Find in Google Scholar

Search WorldCat to find libraries that may hold this journal

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

A Unified Differential Evolution Algorithm for Global Optimization

Journal Article Qiang, Ji; Mitchell, Chad - IEEE Transactions on Evolutionary Computation.

Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
Full Text Available
Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling

Journal Article Vrugt, Jasper; Hyman, James; Robinson, Bruce; ... - International Journal of Nonlinear Sciences and Numerical Simulation

Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can bemore »« less
Full Text Available
Limited‐memory adaptive snapshot selection for proper orthogonal decomposition

Journal Article Oxberry, Geoffrey; Kostova‐Vassilevska, Tanya; Arrighi, William; ... - International Journal for Numerical Methods in Engineering

Summary Reduced order models are useful for accelerating simulations in many‐query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models (ROMs) can have prohibitively expensive memory and floating‐point operation costs in high‐performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time‐stepping ordinary differential equation solvers. The error estimator used in this work is related tomore »« less
Cited by 27
https://doi.org/10.1002/nme.5283
Recursive adaptive-filter design using an adaptive genetic algorithm

Conference Etter, D; Hicks, M; Cho, K

An adaptive genetic algorithm for determining the optimum filter coefficients in a recursive adaptive filter is presented. The algorithm does not use gradient techniques and thus is appropriate for use in problems where the function to be optimized is non-unimodal or non-quadratic, such as the mean-squared error surface in a recursive adaptive filter. The mechanisms of the algorithm are inspired by adaptive processes observed in nature. After an initial set of possible filters is randomly selected, each filter is mapped to a binary string representation. Selected bit strings are then transformed using the operations of crossover and mutation to buildmore »« less
Unified multilevel adaptive finite element methods for elliptic problems

Technical Report Mitchell, W

Many elliptic partial differential equations can be solved numerically with near optimal efficiency through the uses of adaptive refinement and multigrid solution techniques. It is our goal to develop a more unified approach to the combined process of adaptive refinement and multigrid solution which can be with high order finite elements. The basic step of the refinement process is the bisection of a pair of triangles, which corresponds to the addition of one or more basis functions to the approximation space. An approximation of the resulting change in the solution is used as an error indicator to determine which trianglesmore »« less

Similar Records