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  1. 1
    Optimal design of step – cone pulley problem using the bees algorithm

    Optimal design of step – cone pulley problem using the bees algorithm by Yusof, Noor Jazilah, Kamaruddin, Shafie

    Published 2021
    “…Most of these algorithms were developed based on the collective behavior of social swarms of ants, bees, a flock of birds, and schools of fish. …”
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  2. 2
    Application of the bees algorithm for constrained mechanical design optimisation problem

    Application of the bees algorithm for constrained mechanical design optimisation problem by Kamaruddin, Shafie, Abd Latif, Mohd Arif Hafizi

    Published 2019
    “…Nowadays, many optimisation algorithms have been introduced due to the advancement of technology such as Teaching Learning Based Optimisation (TLBO), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO) and the Bees Algorithm. …”
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  3. 3
    Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network

    Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network by Husna, Jamal Abdul Nasir

    Published 2020
    “…Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. …”
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  4. 4
    Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

    Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management by Julius, Beneoluchi Odili, M. N. M., Kahar, Noraziah, Ahmad, M., Zarina, Riaz, Ul Haq

    Published 2017
    “…After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. …”
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  5. 5
    A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

    A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets by Mohd Razali, Muhamad Hasbullah, Saian, Rizauddin, Yap, Bee Wah, Ku-Mahamud, Ku Ruhana

    Published 2021
    “…Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. …”
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  6. 6
    A novel swarm-based optimisation algorithm inspired by artificial neural glial network for autonomous robots

    A novel swarm-based optimisation algorithm inspired by artificial neural glial network for autonomous robots by Ismail, Amelia Ritahani, Tumian, Afidalina

    Published 2019
    “…According to [13], the two best-known swarm intelligence algorithms are Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO). …”
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  7. 7
    The design and applications of the african buffalo algorithm for general optimization problems

    The design and applications of the african buffalo algorithm for general optimization problems by Odili, Julius Beneoluchi

    Published 2017
    “…Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. …”
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  8. 8
    Systematic Review of Enhancement of Artificial Bee Colony Algorithm Using Ant Colony Pheromone

    Systematic Review of Enhancement of Artificial Bee Colony Algorithm Using Ant Colony Pheromone by Alaidi A.H., Der C.S., Leong Y.W.

    Published 2023
    “…The artificial bee colony (ABC) is a well-studied algorithm developed to solve continuous function optimization problems by Karboga and Akay in 2009. …”
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  9. 9
    A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

    A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer by Alsewari, Abdul Rahman Ahmed, Sinan, Q. Salih

    Published 2019
    “…The algorithm is validated based on 36 unconstrained benchmark functions. …”
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  10. 10
    Effectiveness of nature-inspired algorithms using ANFIS for blade design optimization and wind turbine efficiency

    Effectiveness of nature-inspired algorithms using ANFIS for blade design optimization and wind turbine efficiency by Sarkar, Md. Rasel, Julai, Sabariah, Chong, Wen Tong, Toha @ Tohara, Siti Fauziah

    Published 2019
    “…In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony (ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can give the maximum value of Cp for HAWT. …”
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  11. 11
    Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

    Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency by Sarkar, Md Rasel, Julai, Sabariah, Chong, Wen Tong, Toha, Siti Fauziah

    Published 2019
    “…In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony (ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can give the maximum value of Cp for HAWT. …”
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  12. 12
    An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP

    An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP by Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Muzaffar Hamzah, Aida Mustapha, Angela Amphawan

    Published 2021
    “…Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. …”
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  13. 13
    SVM for network anomaly detection using ACO feature subset

    SVM for network anomaly detection using ACO feature subset by Mehmood, T., Rais, H.B.M.

    Published 2016
    “…But irrelevant and redundant features are the obstacle for classification algorithm to build an efficient detection model. This paper proposes a detection model, ant system with support vector machine, which uses ant system, a variation of ant colony optimization, to filter out the redundant and irrelevant features for support vector machine classification algorithm. …”
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  14. 14
    Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm

    Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm by Mohammed Abdullah, Abdullah Nasser

    Published 2018
    “…If t-way strategies are to be adopted in such a system, there is also a need to support test data generation based on sequence of interactions. Addressing these aforementioned issues and complementing the existing sequence based strategies such as t-SEQ, Sequence Covering Array Generator and Bee Algorithm, this thesis presents a unified strategy based on the new meta-heuristic algorithm, called the Elitist Flower Pollination Algorithm (eFPA). …”
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  15. 15
    A review of training methods of ANFIS for applications in business and economic

    A review of training methods of ANFIS for applications in business and economic by Mohd Salleh, Mohd Najib, Hussain, Kashif

    Published 2016
    “…Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance.…”
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  16. 16
    A study on solution of matrix riccati differential equations using ant colony programming and simulink / Mohd Zahurin Mohamed Kamali

    A study on solution of matrix riccati differential equations using ant colony programming and simulink / Mohd Zahurin Mohamed Kamali by Mohamed Kamali, Mohd Zahurin

    Published 2015
    “…In this thesis, we implement the modified ant colony programming (ACP) algorithm for solving the matrix Riccati differential equation (MRDE). …”
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  17. 17
    A review of training methods of ANFIS for applications in business and economics

    A review of training methods of ANFIS for applications in business and economics by Mohd Salleh, Mohd Najib, Hussain, Kashif

    Published 2016
    “…Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance.…”
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  18. 18
    HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION

    HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION by Kappagantula, S., Vojjala, S., Iyer, A.A., Velidi, G., Emani, S., Vandrangi, S.K.

    Published 2023
    “…Swarm Intelligence is inspired by naturally occurring swarm systems suchas Ant Colony, Bees Hive, or Bats. The Bat Algorithm is a population-based meta-heuristic algorithm for solving continuous optimization problems. …”
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  19. 19
    Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System

    Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System by Abidin, Zulkifli Zainal

    Published 2013
    “…It is actually inspired by understanding the decentralized mechanisms in the organization of natural swarms such as the birds, the ants, the bees, the glowworms, and the fireflies. …”
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  20. 20
    Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight

    Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight by Mohamad Razwan, Abdul Malek, Nor Azlina, Ab. Aziz, Alelyani, Salem, Mohana, Mohamed, Farah Nur Arina, Baharudin, Zuwairie, Ibrahim

    Published 2022
    “…Moreover, the comfort level achieved by BA with exponential inertia weight is found to be better than previously reported works using firefly algorithm, genetic algorithm, ant colony optimization, and artificial bee colony algorithm. …”
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