scholarly journals Set-Based Differential Evolution Algorithm Based on Guided Local Exploration for Automated Process Discovery

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Si-Yuan Jing
Keyword(s):  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Differential Evolution Algorithm ◽  
Process Models ◽  
Evolutionary Strategy ◽  
Local Optimum ◽  
Process Discovery ◽  
Event Logs ◽  
Evolution Algorithm ◽  
Automated Process

Evolutionary algorithm is an effective way to solve process discovery problem which aims to mine process models from event logs which are consistent with the real business processes. However, current evolutionary algorithms, such as GeneticMiner, ETM, and ProDiGen, converge slowly and in difficultly because all of them employ genetic crossover and mutation which have strong randomness. This paper proposes a hybrid evolutionary algorithm for automated process discovery, which consists of a set-based differential evolution algorithm and guided local exploration. There are three major innovations in this work. First of all, a hybrid evolutionary strategy is proposed, in which a differential evolution algorithm is employed to search the solution space and rapidly approximate the optimal solution firstly, and then a specific local exploration method joins to help the algorithm skip out the local optimum. Secondly, two novel set-based differential evolution operators are proposed, which can efficiently perform differential mutation and crossover on the causal matrix. Thirdly, a fine-grained evaluation technique is designed to assign score to each node in a process model, which is employed to guide the local exploration and improve the efficiency of the algorithm. Experiments were performed on 68 different event logs, including 22 artificial event logs, 44 noisy event logs, and two real event logs. Moreover, the proposed algorithm was compared with three popular algorithms of process discovery. Experimental results show that the proposed algorithm can achieve good performance and its converge speed is fast.

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.

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.

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.

Circularity Error Evaluation Based on Differential Evolution Algorithm

2011 ◽  
Vol 143-144 ◽  
pp. 416-421
Author(s):  
Dong Xia Wang ◽  
Ai Guo Song ◽  
Xiu Lan Wen ◽  
Feng Lin Wang
Keyword(s):  
Global Convergence ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Differential Evolution Algorithm ◽  
Suggested Method ◽  
Error Evaluation ◽  
Circularity Error ◽  
Minimum Zone ◽  
Evolution Algorithm ◽  
Adaptive Ability

An algorithm based on the differential evolutionary (DE) computation is proposed to evaluate circularity error. It is a heuristic evolutionary algorithm based on population optimization .In the meantime, the suggested method is used to solve the minimum zone circularity error. Compared with other methods, the results show the presented method has very strong self-adaptive ability to environment and better global convergence. Examples proves that the proposed method is effective, convergence and robustness in the process of optimization. And this method makes the circularity error evaluation more accurate.

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.

A Modified Differential Evolution Algorithm for Numerical Optimization Problems

2014 ◽  
Vol 602-605 ◽  
pp. 3585-3588
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Test Functions ◽  
De Algorithm ◽  
Mutation Operation ◽  
Evolution Algorithm ◽  
Pitch Adjustment

To efficiently enhance the global search and local search of Differential Evolution algorithm ( DE), A modified differential evolution algorithm (MDE) is proposed in this paper. The MDE and the DE are different in two aspects. The first is the MDE Algorithm use a strategy of Pitch adjustment instead of original mutation operation, this can enhance the convergence of the MDE, the second is integrate the opposed-learning operation in the crossover operation to prevent DE from being trapped into local optimum. Four test functions are adopted to make comparison with original DE, the MDE has demonstrated stronger velocity of convergence and precision of optimization than differential DE algorithm and PSO.

Modified Differential Evolution Algorithm for Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2536-2539
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Algorithm Performance ◽  
De Algorithm ◽  
Benchmark Experiment ◽  
Random Position ◽  
Evolution Algorithm

In this paper, a modified differential evolution algorithm (MDE) developed to solve unconstrained numerical optimization problems. The MDE algorithm employed random position updating and disturbance operation to replaces the traditional mutation 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 crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the MDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

An Opposition-Based Modified Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 309-313
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Recent Literature ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Algorithm Performance ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm

In this paper, a new opposition-based modified differential evolution algorithm (OMDE) is proposed. This algorithm integrates the opposed-learning operation with the crossover operation to enhance the convergence of the algorithm and to prevent the algorithm from being trapped into the local optimum effectively. Besides, we employed a new strategy to dynamic adjust mutation rate (MR) and crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark functions tested, the OMDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

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