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Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches
Published 2015“…The performance of the ABC algorithm was evaluated through three different onlooker approaches i.e. method 3+0+0 (three onlooker bees are dedicated to the best employee bee), method 2+1+0 (two onlooker bees are dedicated to the best employee bee and one onlooker bee is dedicated to second best employee bee) and method 1+1+1 (one onlooker bee is dedicated to each employee bee). …”
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Thesis -
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Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms
Published 2017“…The modified ABC variants have been developed by inserting new processing stages into the standard ABC algorithm and modifying the employed-bees and onlooker-bees phases to balance out the exploration and exploitation capabilities of the algorithm. …”
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Route Optimization System
Published 2005“…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
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Final Year Project -
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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Fuzzy modeling using Bat Algorithm optimization for classification
Published 2018“…In order to solve it, Bat Algorithm method is implement in to optimization method in fuzzy modeling for classification. …”
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Undergraduates Project Papers -
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Artificial Bee Colony Algorithm for Pairwise Test Generation
Published 2017“…In this paper, we evaluated and proposed a pairwise strategy named Pairwise Artificial Bee Colony algorithm (PABC). …”
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Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics
Published 2018“…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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Optimal route checking using genetic algorithm for UiTM's bus services / Tengku Salman Fathi Tengku Jaafar
Published 2006“…This research study with the development of the Optimal Route Checking Using Genetic Algorithm system should solve this scenario. …”
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Optimize class time tabling by using genetic algorithm technique in UTHM
Published 2019“…This research used genetic algorithm (GA) that was applied to java programming languages with a goal of reducing conflict and optimizing the fitness. …”
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Identifying a Gene Knockout Strategy Using a Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis to Enhance the Production of Succinate and Lacta...
Published 2019“…The objective function of the hybrid algorithm is optimized using a swarm intelligence optimization algorithm and a Simple Constrained Artificial Bee Colony (SCABC) algorithm. …”
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Input-output based relation combinatorial testing using whale optimization algorithm for generating near optimum number of test suite
Published 2025“…This study proposes a combinatorial testing method utilizing the Whale Optimization Algorithm (WOA). …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. …”
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MYMealPal: Malaysian healthy meal planner using artificial bee colony approach / Wan Muhamad Amirul Hakimi Wan Mohd Zaki
Published 2017“…This planner will help user especially Malaysian in planning their daily meal according to the daily calorie requirement. Artificial Bee Colony (ABC) approach is employed as an optimization algorithm in MYMealPal development. …”
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Student Project -
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Review of Multi-Objective Swarm Intelligence Optimization Algorithms
Published 2021“…These reviewed algorithms were mainly developed to solve continuous MOPs. …”
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Comparison of performances of Jaya Algorithm and Cuckoo Search algorithm using benchmark functions
Published 2022“…Metaheuristic algorithms have been used successfully for solving different optimization problems. …”
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Conference or Workshop Item -
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Deterministic and stochastic inventory routing problems with backorders using artificial bee colony / Huda Zuhrah Ab Halim
Published 2019“…The performance of ABC is evaluated on existing datasets and compared with Scatter Search (SS) and Genetic Algorithm (GA). …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…Feature subset selection and classifier ensemble learning are familiar techniques with high ability to optimize above problems. Recently, various techniques based on different algorithms have been developed. …”
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Computational intelligence technique for DG installation within contingency scenario / Muhamad Saifullah Mahmud Affandi
Published 2014“…Meanwhile, for ABC algorithm is inspired of the intelligent behavior of bees during the nectar search process. …”
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Article -
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A framework of test case prioritisation in regression testing using particle swarm-artificial bee colony algorithm
Published 2024“…This research employed a modified version of the Design Science Research Methodology (DSRM), streamlined into five stages: problem identification, theoretical study, framework development, evaluation, and reporting. The developed framework, grounded in fault-based testing theory, comprises three key components: inputs, prioritization factors, and a prioritization algorithm. …”
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