A novel modified binary differential evolution algorithm and its applications

Wang, Ling; Fu, Xiping; Mao, Yunfei; Menhas, Muhammad Ilyas; Fei, Minrui

doi:10.1016/j.neucom.2011.11.033

Cited by 99 publications

(48 citation statements)

References 26 publications

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“…1 (b) presents the vicinity factor values for the reporting cells as well as the non-reporting cells for the given RCP problem. For example, the vicinity factor for cell number 4 which is a reporting cell is calculated by considering the neighbors that are non-reporting cells plus the reporting cell itself (1,2,3,7,8,9,13,5,6,12) which corresponds to the vicinity value of 11. Similarly while calculating the vicinity factor for a non-reporting cell we have to consider the maximum vicinity factor value among the reporting cells from where this cell can be reached.…”

Section: A System Modelmentioning

confidence: 99%

“…For solving the complex optimization problems the differential evolution algorithm has been used in various engineering applications and many works are also carried out to enhance the performance of this algorithm by introducing variants or by modifying it [5][6][7]. Literatures have been reported on nature inspired algorithms for mobility management [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Swayamsiddha¹,

Parija²,

Sahu³

et al. 2017

IJISA

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Abstract-This paper presents binary differential evolution based optimal reporting cell planning (RCP) for location management in wireless cellular networks. The significance of mobile location management (MLM) in wireless communication has evolved drastically due to tremendous rise in the number of mobile users with the constraint of limited bandwidth. The total location management cost involves signaling cost due to location registration and location search and a trade-off between these two gives optimal location management cost. The proposed binary differential evolution (BDE) algorithm is used to determine the optimal reporting cell planning configuration such that the overall mobility management cost is minimized. Evidently, from the simulation result the proposed technique works well for the reference networks in terms of optimal cost and convergence speed. Further the applicability of the BDE is also validated for the realistic network of BSNL (Bharat Sanchar Nigam Limited), Odisha.

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Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Swayamsiddha¹,

Parija²,

Sahu³

et al. 2017

IJISA

View full text Add to dashboard Cite

show abstract

“…Mutation is the most important operator in the performance of the DE algorithm because it generates new elements for the population, which may contain the optimum solution of the objective function [42,43]. The DE algorithm can be summarized as follows [44]:…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Choosing DBSCAN Parameters Automatically using Differential Evolution

Karami¹,

Johansson²

2014

IJCA

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Over the last several years, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has been widely applied in many areas of science due to its simplicity, robustness against noise (outlier) and ability to discover clusters of arbitrary shapes. However, DBSCAN algorithm requires two initial input parameters, namely Eps (the radius of the cluster) and MinPts (the minimum data objects required inside the cluster) which both have a significant influence on the clustering results. Hence, DB-SCAN is sensitive to its input parameters and it is hard to determine them a priori. This paper presents an efficient and effective hybrid clustering method, named BDE-DBSCAN, that combines Binary Differential Evolution and DBSCAN algorithm to simultaneously quickly and automatically specify appropriate parameter values for Eps and MinPts. Since the Eps parameter can largely degrades the efficiency of the DBSCAN algorithm, the combination of an analytical way for estimating Eps and Tournament Selection (TS) method is also employed. Experimental results indicate the proposed method is precise in determining appropriate input parameters of DBSCAN algorithm.

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“…To explore the efficiency of EGBDE, simulations of test functions are carried out using EGBDE, BDE [2] and a binary particle swarm optimization [3] (BPSO). These test functions, whose optimal values are all zeros and these values are met only when the variables are set zeros, are as follows: (1) For EGBDE and BDE, the computing accuracy is set 0.01, and the size of the swarm is 100, with a maximum iteration of 150.…”

Section: Simulation Of Test Functionsmentioning

confidence: 99%

“…Since Hamming distance is not exactly the real-code distance, it is inappropriate to represent the distinction of individuals with the Hamming distance. Zhigang Wang [3] raised a binary differential evolution in which the majority-voting rule is adopted while mutation. Yet this approach is flawed in its randomness while selecting disturbances.…”

Section: Introductionmentioning

confidence: 99%

Elite-Guiding Binary Differential Evolution

Xu¹,

Wang²

2015

Proceedings of the 2015 6th International Conference on Manufacturing Science and Engineering

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Abstract. Differential evolution (DE) is an algorithm highly effective in solving problems with continuous variables while not applicable to problems with discrete variables. To overcome this disadvantage an elite-guiding binary differential evolution (EGBDE) is proposed. Inspired by the mutation with GA which could be dominated by the genes of the fittest chromosomes, a similar process is added to replace the original mutation of DE. Through simulations of test functions and Knapsack Problem (KP) EGBDE is proved to be both feasible and efficient in optimizations of problems with continuous and discrete variables.

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A novel modified binary differential evolution algorithm and its applications

Cited by 99 publications

References 26 publications

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Optimal Reporting Cell Planning with Binary Differential Evolution Algorithm for Location Management Problem

Choosing DBSCAN Parameters Automatically using Differential Evolution

Elite-Guiding Binary Differential Evolution

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