Journal ArticleDOI
Differential evolution with Gaussian mutation and dynamic parameter adjustment
Gaoji Sun,Yanfei Lan,Ruiqing Zhao +2 more
- Vol. 23, Iss: 5, pp 1615-1642
TLDR
This work adopts a novel Gaussian mutation operator and a modified common mutation operator to collaboratively produce new mutant vectors, and employs a periodic function and a Gaussian function to generate the required values of scaling factor and crossover rate, respectively.Abstract:
Differential evolution (DE) is a remarkable evolutionary algorithm for global optimization over continuous search space, whose performance is significantly influenced by its mutation operator and control parameters (scaling factor and crossover rate). In order to enhance the performance of DE, we adopt a novel Gaussian mutation operator and a modified common mutation operator to collaboratively produce new mutant vectors, and employ a periodic function and a Gaussian function to generate the required values of scaling factor and crossover rate, respectively. In the proposed variant of DE (denoted by GPDE), the two adopted mutation operators are adaptively applied to generate the corresponding mutant vector of each individual based on their own cumulative scores, the periodic scaling factor can provide a better balance between exploration ability and exploitation ability, and the Gaussian function-based crossover rate will possess fluctuant value, which possibly enhance the population diversity. To verify the performance of proposed GPDE, a suite of thirty benchmark functions and four real-world problems are applied to conduct the simulation experiment. The simulation results demonstrate that the proposed GPDE performs significantly better than five state-of-the-art DE variants and other two meta-heuristics algorithms.read more
Citations
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Journal ArticleDOI
An adaptive differential evolution with combined strategy for global numerical optimization
TL;DR: This paper proposes a novel DE variant by introducing a series of combined strategies into DE, called CSDE, which achieves the best overall performance among the eight DE variants.
Journal ArticleDOI
Differential evolution: A recent review based on state-of-the-art works
TL;DR: This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years.
Journal ArticleDOI
An adaptive regeneration framework based on search space adjustment for differential evolution
Gaoji Sun,Chunlei Li,Libao Deng +2 more
TL;DR: An adaptive regeneration framework based on search space adjustment (ARSA), which can be easily embedded into various DE variants, which notably improves the performance of two basic DE algorithms and six state-of-the-art DE variants.
Journal ArticleDOI
Differential evolution: A recent review based on state-of-the-art works
TL;DR: Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997 as discussed by the authors .
Journal ArticleDOI
A simple differential evolution with time-varying strategy for continuous optimization
Gaoji Sun,Geni Xu,Nan Jiang +2 more
TL;DR: The experimental results indicate that the proposed TVDE algorithm obtains the best overall performance among the eight DE algorithms.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Journal ArticleDOI
Differential Evolution: A Survey of the State-of-the-Art
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Journal ArticleDOI
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
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