Differential Evolution Algorithm Using Population-Based Homeostasis Difference Vector

2017 ◽  
pp. 579-587 ◽  
Author(s):  
Shailendra Pratap Singh ◽  
Anoj Kumar
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Difference Vector ◽  
Evolution Algorithm

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM

2002 ◽  
Vol 11 (04) ◽  
pp. 531-552 ◽  
Author(s):  
H. A. ABBASS ◽  
R. SARKER
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Standard Test ◽  
Test Problems ◽  
Pareto Optimal Solutions ◽  
Continuous Domains ◽  
Evolution Algorithm ◽  
Multiobjective Algorithms

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto Differential Evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for five standard test problems, is competitive to nine known evolutionary multiobjective algorithms for solving VOPs.

scholarly journals Population based optimization via differential evolution and adaptive fractional gradient descent

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5173-5185
Author(s):  
Zijian Liu ◽  
Chunbo Luo ◽  
Peng Ren ◽  
Tingwei Wang ◽  
Geyong Min
Keyword(s):  
Differential Evolution ◽  
Gradient Descent ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Parameters Estimation ◽  
System Response ◽  
Local Optimum ◽  
Value Functions ◽  
Selection Processes ◽  
Evolution Algorithm

We propose a differential evolution algorithm based on adaptive fractional gradient descent (DE-FGD) to address the defects of existing bio-inspired algorithms, such as slow convergence speed and local optimum. The crossover and selection processes of the differential evolution algorithm are discarded and the adaptive fractional gradients are adopted to enhance the global searching capability. For the benchmark functions, our proposed algorithm Specifically, our method has higher searching accuracy than several state of the art bio-inspired algorithms. Furthermore, we apply our method to specific tasks - parameters estimation of system response functions and approximate value functions. Experiment results validate that our proposed algorithm produces accurate estimations and improves searching efficiency

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