scholarly journals Adaptive Unified Differential Evolution for Clustering

2018 ◽  
Vol 12 (1) ◽  
pp. 53
Author(s):  
Maulida Ayu Fitriani ◽  
Aina Musdholifah ◽  
Sri Hartati
Keyword(s):  
Differential Evolution ◽  
Fitness Function ◽  
Single Mutation ◽  
Clustering Methods ◽  
Mutation Strategy ◽  
Clustering Quality ◽  
Silhouette Index ◽  
Mutation Strategies ◽  
Time Required

Various clustering methods to obtain optimal information continues to evolve one of its development is Evolutionary Algorithm (EA). Adaptive Unified Differential Evolution (AuDE), is the development of Differential Evolution (DE) which is one of the EA techniques. AuDE has self adaptive scale factor control parameters (F) and crossover-rate (Cr).. It also has a single mutation strategy that represents the most commonly used standard mutation strategies from previous studies.The AuDE clustering method was tested using 4 datasets. Silhouette Index and CS Measure is a fitness function used as a measure of the quality of clustering results. The quality of the AuDE clustering results is then compared against the quality of clustering results using the DE method.The results show that the AuDE mutation strategy can expand the cluster central search produced by ED so that better clustering quality can be obtained. The comparison of the quality of AuDE and DE using Silhoutte Index is 1:0.816, whereas the use of CS Measure shows a comparison of 0.565:1. The execution time required AuDE shows better but Number significant results, aimed at the comparison of Silhoutte Index usage of 0.99:1 , Whereas on the use of CS Measure obtained the comparison of 0.184:1.

scholarly journals Data Clustering based on Modified Differential Evolution and Quasi-Opposition-based Learning

2020 ◽  
Vol 13 (6) ◽  
pp. 168-178
Author(s):  
Pyae Cho ◽  
◽  
Thi Nyunt ◽  
Keyword(s):  
Differential Evolution ◽  
Heuristic Search ◽  
Clustering Algorithm ◽  
Population Based ◽  
Initial Population ◽  
Local Optima ◽  
Opposition Based Learning ◽  
De Algorithm ◽  
Mutation Strategy

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.

A Novel Mutation Strategy for Differential Evolution

2016 ◽  
pp. 20-31 ◽  
Author(s):  
Hira Zaheer ◽  
Millie Pant ◽  
Sushil Kumar ◽  
Oleg Monakhov
Keyword(s):  
Differential Evolution ◽  
Novel Mutation ◽  
Optimization Technique ◽  
Search Space ◽  
Additional Parameter ◽  
Solution Vector ◽  
Mutation Strategy ◽  
Suggested Strategy ◽  
The Difference ◽  
Mutation Strategies

Differential Evolution (DE) has attained the reputation of a powerful optimization technique that can be used for solving a wide range of problems. In DE, mutation is the most important operator as it helps in generating a new solution vector. In this paper we propose an additional mutation strategy for DE. The suggested strategy is named DE/rand-best/2. It makes use of an additional parameter called guiding force parameter K, which takes a value between (0,1) besides using the scaling factor F, which has a fixed value. De/rand-best/2 makes use of two difference vectors, where the difference is taken from the best solution vector. One vector difference will be produced with a randomly generated mutation factor K (0,1). It will add a different vector to the old one and search space will increase with a random factor. Result shows that this strategy performs well in comparison to other mutation strategies of DE.

scholarly journals An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Wan-li Xiang ◽  
Xue-lei Meng ◽  
Mei-qing An ◽  
Yin-zhen Li ◽  
Ming-xia Gao
Keyword(s):  
Differential Evolution ◽  
Linear Time ◽  
Differential Evolution Algorithm ◽  
Premature Convergence ◽  
Perturbation Scheme ◽  
Solution Quality ◽  
Multiple Mutation ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Mutation Strategies

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

An improved ant algorithm with LDA-based representation for text document clustering

2016 ◽  
Vol 43 (2) ◽  
pp. 275-292 ◽  
Author(s):  
Aytug Onan ◽  
Hasan Bulut ◽  
Serdar Korukoglu
Keyword(s):  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Clustering Methods ◽  
Initial Value ◽  
Text Document ◽  
Clustering Quality ◽  
Text Features

Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are frequently applied to clustering. In this paper, an improved ant clustering algorithm is presented, where two novel heuristic methods are proposed to enhance the clustering quality of ant-based clustering. In addition, the latent Dirichlet allocation (LDA) is used to represent textual documents in a compact and efficient way. The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents.

A Hybrid Differential Evolution Algorithm for Solving Function Optimization

2010 ◽  
Vol 439-440 ◽  
pp. 315-320
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Differential Evolution ◽  
Novel Mutation ◽  
Experimental Studies ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Test Cases ◽  
Key Points ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Mutation Strategies

One of the key points resulting in the success of differential evolution (DE) is its mechanism of different mutation strategies for generating mutant vectors. In this paper, we also present a novel mutation strategy inspired by the velocity updating scheme of particle swarm optimization (PSO). The proposed approach is called HDE, which conducts the mutation strategy on the global best vector for each generation. Experimental studies on 8 well-known benchmark functions show that HDE outperforms other three compared DE algorithms in most test cases.

scholarly journals Ranking and classification of cryptocurrency exchanges using the methods of a multidimensional comparative analysis

2021 ◽  
Vol 21 (2) ◽  
pp. 38-56
Author(s):  
Kinga Kądziołka
Keyword(s):  
Comparative Analysis ◽  
Hierarchical Clustering ◽  
Research Methodology ◽  
Composite Indicators ◽  
Clustering Methods ◽  
Multidimensional Assessment ◽  
Silhouette Index

Abstract Research background: The multidimensional assessment of the attractiveness of cryptocurrency exchanges seems to be an important issue, because the risk of the collapse of such an exchange or its use for illegal purposes is higher than in the case of traditional exchanges. Purpose: The aim of the work is to create ranking and identify groups of cryptocurrency exchanges with a similar level of attractiveness. Research methodology: 13 different composite indicators were considered. Finally, one of them was chosen as a representative according to the similarity of the obtained rankings. Clustering methods were used to identify groups of exchanges with a similar level of the constructed measure. Result: The best according to the adopted criteria of rankings similarity was the taxonomic measure constructed using the standardized sum method with equal weights. Combining hierarchical clustering with the k-means algorithm allowed to improve the quality of clustering measured with the silhouette index. Novelty: The originality of the paper lies in the use of different methods of a multidimensional comparative analysis on the cryptocurrency market.

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Global Numerical Optimization

2013 ◽  
Vol 457-458 ◽  
pp. 1283-1287 ◽  
Author(s):  
Si Ling Feng ◽  
Qing Xin Zhu ◽  
Sheng Zhong ◽  
Xiu Jun Gong
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Global Numerical Optimization ◽  
De Algorithm ◽  
Mutation Operators ◽  
Wide Range ◽  
The Cost

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solution. Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. In this paper, we applied a hybridization of adaptive BBO with DE approach, namely ABBO/DE/GEN, for the global numerical optimization problems. ABBO/DE/GEN adaptively changes migration probability and mutation probability based on the relation between the cost of fitness function and average cost every generation, and the mutation operators of BBO were modified based on DE algorithm and the migration operators of BBO were modified based on number of iteration to improve performance. And hence it can generate the promising candidate solutions. To verify the performance of our proposed ABBO/DE/GEN, 9 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with BBO/DE/GEN approaches, ABBO/DE/GEN performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate.

scholarly journals Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Quoc-Lam Nguyen ◽  
Quang-Nhat Pham
Keyword(s):  
Differential Evolution ◽  
Critical Path ◽  
Labor Demand ◽  
Project Planning ◽  
Decision Makers ◽  
Project Schedule ◽  
Planning Project ◽  
Mutation Strategy ◽  
Mutation Strategies ◽  
Labor Utilization

In construction management, the task of planning project schedules with consideration of labor utilization is very crucial. However, the commonly used critical path method (CPM) does not inherently take into account this issue. Consequently, the labor utilization of the project schedule derived from the CPM method often has substantial low ebbs and high peaks. This research proposes a model to obtain project schedule with the least fluctuation in labor demand while still satisfying the project deadline and maintain the project cost. The Differential Evolution (DE), a fast and efficient metaheuristic, is employed to search for the most desirable solution of project execution among numerous combinations of activities’ crew sizes and start times. Furthermore, seven DE’s mutation strategies have also been employed for solving the optimization at hand. Experiment results point out that theTarget-to-Best 1and a new hybrid mutation strategy can attain the best solution of project schedule with the least fluctuation in labor demand. Accordingly, the proposed framework can be an effective tool to assist decision-makers in the project planning phase.

scholarly journals Dual Mutations Collaboration Mechanism with Elites Guiding and Inferiors Eliminating Techniques for Differential Evolution

2021 ◽  
Author(s):  
Libao Deng ◽  
Chunlei Li ◽  
Haili Sun ◽  
Liyan Qiao ◽  
Xiaodong Miao
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Evaluation Process ◽  
Distribution Model ◽  
Fitness Evaluation ◽  
De Algorithm ◽  
Mutation Strategy ◽  
Base Vector ◽  
The Difference ◽  
Mutation Strategies

Abstract Differential Evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.

Heat Transfer Enhancement in Separation Process of Ethanol from Ethanol Water Mixture by Using Surfactants

2020 ◽  
pp. 9-14 ◽  
Author(s):  
Acharya Anil Ramchandra ◽  
R. Kadam ◽  
A. T. Pise
Keyword(s):  
Heat Transfer ◽  
Ammonium Chloride ◽  
Sodium Dodecyl ◽  
Water Mixture ◽  
Transfer Enhancement ◽  
Surface Tension Reduction ◽  
Surface Active Agents ◽  
Surface Active ◽  
Time Required

Here the investigations are done while distillation of ethanol-water mixture for separating ethanol from fermentation process. Focus is to study reduction in time required and hence saving in energy for the distillation process of ethanol-water mixture under the influence of surface-active agents (Surfactants). This novelty is from observation of these surfactants to enhance heat transfer rate because of surface tension reduction in aqueous solutions. SDS (Sodium Dodecyl Sulphate), NH4Cl (Ammonium Chloride) and SLBS (Sodium lauryl benzene sulphonate) surfactants in different concentration are experimented. The concentration of these surfactant is varied from 1700 ppm to 2800 ppm. This range is decided by observing critical micelle concentration of used surfactants. Results showed that time is reduced and hence energy consumption is also reduced. Results shown by NH4Cl are found to be more useful as it is ecofriendly surfactant which is not affecting ethanol-water mixture. Use of ammonium chloride as surfactant in distillation is actually useful to reduce energy without hampering the quality of process is the novelty of this work.

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