A Data Clustering Approach Using Bees Algorithm with a Memory Scheme

Nemmich, Mohamed Amine; Debbat, Fatima; Slimane, Mohamed

doi:10.1007/978-3-319-98352-3_28

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…Qaddoura et al (2021) developed an evolutionary algorithm based on the evolutionary behavior of a genetic algorithm combined with the nearest neighbor search technique for clustering problems. Nemmich et al (2019) used the Bees algorithm with a Memory Scheme to solve data clustering problems. All approaches were tested on several benchmark data sets and sometimes real-life problems, and the authors considered various statistical tests to justify the effectiveness of the suggested approaches.…”

Section: Clustering Methodsmentioning

confidence: 99%

“…Exploration and exploitation capacities. Centroids position Others clustering Algorithms & Bees algorithm with memory scheme (Nemmich et al, 2019) Cat swarm optimization (CSO) (Singh & Kumar, 2020) cuttlefish algorithm (CFA) (Eesa & Orman, 2020) Firefly algorithm (Ezugwu et al, 2020) flower pollination algorithm (Dhal et al, 2020) GA (Dutta et al, 2019;Qaddoura et al, 2021) GWO and PSO (Zhang et al, 2021) whale optimization algorithm (Singh, 2021) Classification Error rate minimization. Accuracy improvement.…”

Section: Optimum Number Of Centroids Fuzzy C-meansmentioning

confidence: 99%

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Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

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Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.

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Section: Clustering Methodsmentioning

confidence: 99%

Section: Optimum Number Of Centroids Fuzzy C-meansmentioning

confidence: 99%

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Azevedo,

Rocha,

Pereira

2024

Mach Learn

View full text Add to dashboard Cite

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“…This information is very important and affects the way forager bees (both scout and follower bees) choose the patch to visit and whether it will be based on a waggle dance or not (i.e., choosing a familiar food source). Foragers depend on their own memories to discover particular locations during the visit of food patches repeatedly [16,19].…”

Section: Improved Bees Algorithm With Memory Scheme and Adaptive Neighborhoodmentioning

confidence: 99%

“…The BA has been successfully applied in many different engineering problems, such as supply chain optimization [8], production scheduling [9], numerical functions optimization [5,6,7,10], solving timetabling problems [11], control system tuning [12], protein conformation search [13], test form construction [14], Placements of FACTS devices [15], pattern recognition [16], robotic swarm coordination [17], data mining [18,19,20], chemical process [21], mechanical design [22], wood defect classification [23], Printed Circuit Board (PCB) assembly optimization [24], image analysis [25], and many other applications [26].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Bees Approach based on Improved Search Sites Selection by Firefly Algorithm for Solving Complex Continuous Functions

Nemmich

Debbat

Slimane

2020

IJCAI

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The Bees Algorithm (BA) is a recent population-based optimization algorithm, which tries to imitate the natural behavior of honey bees in food foraging. This meta-heuristic is widely used in various engineering fields. However, it suffers from certain limitations. This paper focuses on improvements to the BA in order to improve its overall performance. The proposed enhancements were applied alone or in pair to develop enhanced versions of the BA. Three improved variants of BA were presented: BAMS-AN, HBAFA and HFBA. The new BAMS-AN includes memory scheme in order to avoid revisiting previously visited sites and an adaptive neighborhood search procedure to escape from local optima during the local search process. HBAFA introduces the Firefly Algorithm (FA) in local search of BA to update the positions of recruited bees, thus increasing exploitation in each selected site. The third improved BA, i.e. HFBA, employs FA to initialize the population of bees in the BA for a best exploration and to start the search from more promising regions of the search space. The proposed enhancements to the BA have been tested using several continuous benchmark functions and the results have been compared to those achieved by the standard BA and other optimization techniques. The experimental results indicate that the improved variants of BA outperform the standard BA and other algorithms on most of the benchmark functions. The enhanced BAMS-AN performs particularly better than others improved BAs in terms of solution quality and convergence speed.Povzetek: Za reševanje kompleksnih zveznih funkcij so razvili nov pristop na osnovi hibridnega čebeljega algoritma (BA) in algoritma Firefly.

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“…Inrecentyears,theBAhasbeenwidelyandsuccessfullyappliedonavarietyofengineering problems,suchaspatternrecognition (Nebti&Boukerram,2013),datamining (Poonam&Dhaiya 2015;Nemmich, Debbat, & Slimane, 2019), supply chain optimisation (Mayteekrieangkrai & Wongthatsanekorn,2015),imageanalysis (Azarbadetal.,2011),numericalfunctionsoptimisation (Pham & Castellani, 2009;Pham et al, 2006a;Yuce et al, 2013;Nemmich & Debbat, 2017;Nemmich,Debbat&Slimane,2018),solvingtimetablingproblems(NguyenK.,NguyenP.,&Tran, 2012),productionscheduling(Packianatheretal.,2014,controlsystemtuning (Pham,Darwish, & Eldukhri, 2009), robotic swarm coordination (Jevtić, Gutiérrez, Andina, & Jamshidi, 2012), proteinconformationsearch(Janaetal.,2015,wooddefectclassification (Packianather&Kapoor, 2015),testformconstruction (Songmuang&Ueno,2011),mechanicaldesign(Moradietal.,2015, chemicalprocess (Castellani,Q.T.Pham,&D.T.Pham,2012),PrintedCircuitBoard(PCB)assembly optimisation (Pham,Otri,&Darwish,2007b),PlacementsofFACTSdevices (Idris,Kharuddi,& Mustafa2009a),andseveralotherapplications (Hussein,Sahran&SheikhAbdullah,2017). TheBAhasgraspedaremarkableattentionofresearcherssinceithasbeenprovedtobeefficient androbustoptimizationtool.Itseemsthattheymaybeusefulforsolvingresourceconstrainedproject schedulingproblem(RCPSP).…”

Section: Introductionmentioning

confidence: 99%

A Permutation-Based Bees Algorithm for Solving Resource-Constrained Project Scheduling Problem

Nemmich

Debbat

Slimane

2019

International Journal of Swarm Intelligence Research

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In this article, a novel Permutation-based Bees Algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The PBA is a modification of existing Bees Algorithm (BA) adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts. The algorithm treats the solutions of RCPSP as bee swarms and employs the activity-list representation and moves operators for the bees, in association with the serial scheduling generation scheme (Serial SGS), to execute the intelligent updating process of the swarms to search for better solutions. The performance of the proposed approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLIB and compared with other approaches from the literature. Simulation results demonstrate that the proposed PBA provides an effective and efficient approach for solving RCPSP.

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A Data Clustering Approach Using Bees Algorithm with a Memory Scheme

Cited by 7 publications

References 11 publications

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Hybrid Bees Approach based on Improved Search Sites Selection by Firefly Algorithm for Solving Complex Continuous Functions

A Permutation-Based Bees Algorithm for Solving Resource-Constrained Project Scheduling Problem

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