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  1. 1
    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm by Abro, Abdul Ghani

    Published 2013
    “…To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum. …”
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  2. 2
    Web-Based Route Optimization System For Logistic Using Agglomerative Clustering And Genetic Algorithm

    Web-Based Route Optimization System For Logistic Using Agglomerative Clustering And Genetic Algorithm by Tan, Chee Kuan

    Published 2020
    “…This system will first cluster those addresses based on the number of trucks involved and then perform the route optimization algorithm to suggest the shortest route to the user. …”
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  3. 3
    A trade-off criterion for bi-objective problem in solving hybrid flow shop scheduling with energy efficient (EE-HFS) using multi-objective dragonfly algorithm (MODA)

    A trade-off criterion for bi-objective problem in solving hybrid flow shop scheduling with energy efficient (EE-HFS) using multi-objective dragonfly algorithm (MODA) by Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

    Published 2024
    “…The optimization result was compared with well-established algorithms, the Pareto Envelope-based Selection Algorithm II (PESA2), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and new algorithms, Multi-Objective Grasshopper Optimization Algorithm (MOGOA) and Multi-Objective Ant Lion Optimizer (MOALO). …”
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  4. 4
    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman

    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman by Azman, Alya Kauthar

    Published 2025
    “…This study applies Genetic Algorithms (GA) to optimize space distribution for test scheduling, addressing the challenge of managing multiple test sessions across distinct locations. …”
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  5. 5
    Agreement options for negotiation on material location decision of housing development

    Agreement options for negotiation on material location decision of housing development by Utomo, C., Rahmawati, Y.

    Published 2020
    “…Application of coalition formation for agreement options is used to select the best fit material location. It is based on satisficing game theory rather than optimization. …”
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  6. 6
    Dynamic positioning base station for wireless sensor network using particle swarm optimization (PSO)

    Dynamic positioning base station for wireless sensor network using particle swarm optimization (PSO) by Nurul Adilah Abdul Latiff

    Published 2012
    “…This creates unbalanced energy consumption among all sensor nodes and furthermore reduces the network energy efficiency. Since the optimal selection of base station location in a network belongs to nondeterministic polynomial (NP) hard problem, the use of approximation algorithms such as Particle Swarm Optimization (PSO) are generally more suitable due to its simplicity and outstanding search strength.…”
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  7. 7
    Mobile data gathering algorithms for wireless sensor networks

    Mobile data gathering algorithms for wireless sensor networks by Ghaleb, Mukhtar Mahmoud Yahya

    Published 2014
    “…These mobile elements will go to each transmission range of each respective sensor to upload its data. In this research, a Mobile Data Gathering based Network Layout (MDG-NL) algorithm is proposed. …”
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  8. 8
    Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array

    Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array by Abdul Rani, Khairul Najmi, Abdulmalek, Mohamed Fareq, Rahim, Hasliza, Siew Chin, Neoh, Abd Wahab, Alawiyah

    Published 2017
    “…In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively.All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler–Deb–Thiele’s (ZDT’s) test functions. …”
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  9. 9
    A load-balanced algorithm for Internet Gateway placement in Backbone Wireless Mesh Networks

    A load-balanced algorithm for Internet Gateway placement in Backbone Wireless Mesh Networks by Mohammed, Najib Ahmed, Othman, Mohamed

    Published 2024
    “…The algorithm distributes WMRs among IGWs based on their location and computation degree, considering the load balance between IGWs and WMRs in each cluster. …”
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  10. 10
    Development of integrated models for distribution network design of perishable products

    Development of integrated models for distribution network design of perishable products by Firoozi, Zahra

    Published 2015
    “…A Lagrangian relaxationbased heuristic algorithm is developed to solve the model. The algorithm produces solutions that are within 0.027% of optimality gap.…”
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  11. 11
    Crossover and mutation operators of real coded genetic algorithms for global optimization problems

    Crossover and mutation operators of real coded genetic algorithms for global optimization problems by Lim, Siew Mooi

    Published 2016
    “…GA operators employ two main strategies; that is, exploration and exploitation to locate the optimum solutions. This research employed a new generational GA based on a combination of the proposed Rayleigh Crossover (RX) and proposed Scale Truncated Pareto Mutation (STPM) called RX-STPM. …”
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  12. 12
    Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning

    Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning by Costache R., Pal S.C., Pande C.B., Islam A.R.M.T., Alshehri F., Abdo H.G.

    Published 2025
    “…In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk Optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk Optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk Optimization-Index of Entropy (Stacking-HHO-IOE). …”
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  13. 13
    Hybridization Of Deterministic And Metaheuristic Approaches In Global Optimization

    Hybridization Of Deterministic And Metaheuristic Approaches In Global Optimization by Goh, Khang Wen

    Published 2019
    “…In the analysis of the literature, Artificial Bees Colony (ABC) Algorithm has been selected as the metaheuristic approach to be improved its capability and efficiency to solve the global optimization problems. …”
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  14. 14
    Optimized techniques for landslide detection and characteristics using LiDAR data

    Optimized techniques for landslide detection and characteristics using LiDAR data by Mezaal, Mustafa Ridha

    Published 2018
    “…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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  15. 15
    Efficient vehicle routing optimization for autistic users / Abdulrahman Hasan Mohammed Shabalah

    Efficient vehicle routing optimization for autistic users / Abdulrahman Hasan Mohammed Shabalah by Abdulrahman Hasan , Mohammed Shabalah

    Published 2020
    “…Therefore, in this research, a routing algorithm for ride-hailing services has been proposed. …”
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  16. 16
    Data transmission in wireless sensor network with greedy function and particle swarm optimization

    Data transmission in wireless sensor network with greedy function and particle swarm optimization by Hamzarul Alif Hamzah, Norah Tuah, Kit Guan Lim, Min Keng Tan, Lei Zhu, Kenneth Tze Kin Teo

    Published 2019
    “…For this research, PEGASIS is selected for its high energy-efficiency and compatibility with the proposed system. …”
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  17. 17
    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed

    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

    Published 2012
    “…The application of FES optimized by GA on regionalization creates opportunities for further researches which utilizes different types of optimization like Ant Colony Optimization (ACO), ANN’s, Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA).…”
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  18. 18
    An adaptive localization system using particle swarm optimization in a circular distribution form

    An adaptive localization system using particle swarm optimization in a circular distribution form by Alhammadi, Abdulraqeb, Hashim, Fazirulhisyam, Fadlee, Mohd, Shami, Tareq M.

    Published 2016
    “…One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. …”
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  19. 19
    Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering

    Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering by Annisa Eka Haryati, ., Sugiyarto, Surono, Tommy Tanu, Wijaya, Goh, Khang Wen, Aris, Thobirin

    Published 2022
    “…Result: The results showed that the most optimal number of clusters is 3, which are selected based on the largest PC value. …”
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  20. 20
    Energy efficient cluster head distribution in wireless sensor networks

    Energy efficient cluster head distribution in wireless sensor networks by Siew, Zhan Wei

    Published 2013
    “…Thus, the best selected CHs location with network global information using FLCH and PSO will be announced by the base station (BS) to the sensor nodes. …”
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