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

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.

scholarly journals Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Betania Hernández-Ocaña ◽  
Ma. Del Pilar Pozos-Parra ◽  
Efrén Mezura-Montes ◽  
Edgar Alfredo Portilla-Flores ◽  
Eduardo Vega-Alvarado ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Mechanical Design ◽  
Differential Evolution Algorithm ◽  
Fine Tuning ◽  
Local Optimum ◽  
Bacterial Foraging Optimization ◽  
Planar Mechanisms ◽  
Bacterial Foraging ◽  
Evolution Algorithm ◽  
Parameter Values

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

Intrusion Detection Based on Dynamic Gemini Population DE-K-mediods Clustering on Hadoop Platform

2020 ◽  
pp. 2150001
Author(s):  
Wentie Wu ◽  
Shengchao Xu
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Differential Evolution ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection System ◽  
Differential Evolution Algorithm ◽  
Detection Algorithm ◽  
Local Optimum ◽  
Evolution Algorithm

In view of the fact that the existing intrusion detection system (IDS) based on clustering algorithm cannot adapt to the large-scale growth of system logs, a K-mediods clustering intrusion detection algorithm based on differential evolution suitable for cloud computing environment is proposed. First, the differential evolution algorithm is combined with the K-mediods clustering algorithm in order to use the powerful global search capability of the differential evolution algorithm to improve the convergence efficiency of large-scale data sample clustering. Second, in order to further improve the optimization ability of clustering, a dynamic Gemini population scheme was adopted to improve the differential evolution algorithm, thereby maintaining the diversity of the population while improving the problem of being easily trapped into a local optimum. Finally, in the intrusion detection processing of big data, the optimized clustering algorithm is designed in parallel under the Hadoop Map Reduce framework. Simulation experiments were performed in the open source cloud computing framework Hadoop cluster environment. Experimental results show that the overall detection effect of the proposed algorithm is significantly better than the existing intrusion detection algorithms.

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 Application of Improved Differential Evolution Algorithm in Solving Equations

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guiying Ning ◽  
Yongquan Zhou
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Convergence Speed ◽  
Evolutionary Strategy ◽  
Adaptive Mutation ◽  
Local Optimum ◽  
Algebraic Equations ◽  
Evolution Algorithm ◽  
Roots Of Equations ◽  
Improved Differential Evolution Algorithm

AbstractThe problem of finding roots of equations has always been an important research problem in the fields of scientific and engineering calculations. For the standard differential evolution algorithm cannot balance the convergence speed and the accuracy of the solution, an improved differential evolution algorithm is proposed. First, the one-half rule is introduced in the mutation process, that is, half of the individuals perform differential evolutionary mutation, and the other half perform evolutionary strategy reorganization, which increases the diversity of the population and avoids premature convergence of the algorithm; Second, set up an adaptive mutation operator and a crossover operator to prevent the algorithm from falling into the local optimum and improve the accuracy of the solution. Finally, classical high-order algebraic equations and nonlinear equations are selected for testing, and compared with other algorithms. The results show that the improved algorithm has higher solution accuracy and robustness, and has a faster convergence speed. It has outstanding effects in finding roots of equations, and provides an effective method for engineering and scientific calculations.

Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm

2017 ◽  
Vol 11 (1) ◽  
pp. 177-192
Author(s):  
Gonggui Chen ◽  
Zhengmei Lu ◽  
Zhizhong Zhang ◽  
Zhi Sun
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Power Losses ◽  
De Algorithm ◽  
Optimal Power ◽  
Evolution Algorithm

Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

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