scholarly journals An Adaptive Differential Evolution Algorithm with Restart for Solving Continuous Optimization Problems

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
General Purpose ◽  
Adaptive Parameter ◽  
Adaptive Differential Evolution ◽  
Use Efficiency ◽  
Evolution Algorithm ◽  
Continuous Optimization Problems

A new adaptive differential evolution algorithm with restart (ADE-R) is proposed as a general-purpose method for solving continuous optimization problems. Its design aims at simplicity of use, efficiency and robustness. ADE-R simulates a population evolution of real vectors using vector mixing operations with an adaptive parameter control based on the switching of two selected intervals of values for each scaling factor and crossover rate of the basic differential evolution algorithm. It also incorporates a restart technique to supply new contents to the population to prevent premature convergence and stagnation. The method is tested on several benchmark functions covering various types of functions and compared with some well-known and state-of-art methods. The experimental results show that ADE-R is effective and outperforms the compared methods.

scholarly journals Multiscale Cooperative Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yongzhao Du ◽  
Yuling Fan ◽  
Xiaofang Liu ◽  
Yanmin Luo ◽  
Jianeng Tang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Early Stage ◽  
Differential Evolution Algorithm ◽  
Search Range ◽  
Adaptive Parameter ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
The Individual

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.

scholarly journals Hypercube optimization based global solution in numerical benchmark and sensor localization

2018 ◽  
Vol 7 (2.6) ◽  
pp. 88
Author(s):  
S R.Sujatha ◽  
M Siddappa
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Dynamic Weight ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Self Adaptive

An original learning algorithm for solving global numerical optimization problems is proposed. The proposed algorithm is strong stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The hypercube optimization algorithm includes the initialization and evaluation process, and searching space process. The designed HO algorithm is tested on specific benchmark functions. The comparative performance analysis have made against with other approaches of dynamic weight particle swarm optimization and self-adaptive differential evolution algorithm. Convergence characteristics of self-adaptive differential evolution algorithm has deliver the much better functional   value in compare to dynamic weight based particle swarm optimization.

scholarly journals pSum-SaDE: A Modifiedp-Median Problem and Self-Adaptive Differential Evolution Algorithm for Text Summarization

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Rasim M. Alguliev ◽  
Ramiz M. Aliguliyev ◽  
Chingiz A. Mehdiyev
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Median Problem ◽  
Proposed Model ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Self Adaptive

Extractive multidocument summarization is modeled as a modifiedp-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.

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