Search Results - optimal ((((bat algorithm) OR (((acs algorithm) OR (ant algorithm))))) OR (tree algorithm))

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  1. 1
    IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks

    IWDSA: a hybrid Intelligent Water Drops with a Simulated Annealing for the localization improvement in wireless sensor networks by Gumaida, Bassam, Ibrahim, Adamu Abubakar

    Published 2024
    “…Additionally, simulation results confirm that the proposed algorithm IWDSA exhibits outstanding performance compared to other algorithms utilizing optimization techniques, including genetic algorithms, bat algorithms, ant colony optimization, and swarm optimization. …”
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  2. 2
    Ant colony optimization in dynamic environments

    Ant colony optimization in dynamic environments by Chen, Fei Huang

    Published 2010
    “…In order to achieve this objective, six ant algorithms namely Ant System (AS), Ant Colony System (ACS), Best-Worst Ant System (BWAS), Elitist Ant System (EAS), Max-Min Ant System (MMAS) and Rank-Based Ant System (RBAS) were implemented to solve a dynamic optimization problem in the form of the dynamic Traveling Salesman Problem (TSP). …”
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  3. 3
    Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm

    Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm by Muhammad Syahril Mohamad Sainal

    Published 2022
    “…Meta-heuristic algorithms are mostly used to solve this problem. For example, Genetic Algorithm with Particle Swarm Optimization, Genetic Algorithm with Artificial Bee Colony Algorithms (ABC) and Genetic Algorithm with Ant Colony Optimization Algorithms. …”
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  4. 4
    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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  5. 5
    Balancing exploration and exploitation in ACS algorithms for data clustering

    Balancing exploration and exploitation in ACS algorithms for data clustering by Jabbar, Ayad Mohammed, Sagban, Rafid, Ku-Mahamud, Ku Ruhana

    Published 2019
    “…Ant colony optimization (ACO) is a swarm algorithm inspired by different behaviors of ants. …”
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  6. 6
    Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system

    Guidance system based on Dijkstra-ant colony algorithm with binary search tree for indoor parking system by Mohammad Ata, Karimeh Ibrahim, Che Soh, Azura, Ishak, Asnor Juraiza, Jaafar, Haslina

    Published 2021
    “…In this paper, the Dijkstra-ant colony algorithm (ACO) with binary search tree (BST) has been proposed. …”
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  7. 7
    Comparative Performance Analysis of Bat Algorithm and Bacterial Foraging Optimization Algorithm using Standard Benchmark Functions

    Comparative Performance Analysis of Bat Algorithm and Bacterial Foraging Optimization Algorithm using Standard Benchmark Functions by Mazlina, Abdul Majid, Alsariera, Yazan A., Alamri, Hammoudeh S., Nasser, Abdullah M., Kamal Z., Zamli

    Published 2014
    “…Over the last 30 years, many meta-heuristic algorithms have been developed in the literature including that of Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HS) to name a few. …”
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  8. 8
    Adaptive parameter control strategy for ant-miner classification algorithm

    Adaptive parameter control strategy for ant-miner classification algorithm by Al-Behadili, Hayder Naser Khraibet, Sagban, Rafid, Ku-Mahamud, Ku Ruhana

    Published 2020
    “…This paper presents a new hybrid Ant-Miner classification algorithm and ant colony system (ACS), called ACS-Ant Miner. …”
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  9. 9
    Embedding Malaysian House Red Ant Behavior into an Ant Colony System

    Embedding Malaysian House Red Ant Behavior into an Ant Colony System by Ali Othman, Zulaiha, Md Rais, Helmi, Hamdan, Abdul Razak

    Published 2008
    “…This research aims to improve the algorithm by embedding individual Malaysian House Red Ant behavior into ACS. …”
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  10. 10
    An Experiment of Ant Algorithms : Case Study of Kota Kinabalu Central Town

    An Experiment of Ant Algorithms : Case Study of Kota Kinabalu Central Town by Nor Rafidah, Mohamad

    Published 2005
    “…The objectives of this study are to explore and evaluate the Ant System (AS) algorithm and Ant Colony System (ACS) algorithm in finding shortest paths. …”
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  11. 11
    Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm

    Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm by Chong K.L., Lai S.H., Ahmed A.N., Wan Jaafar W.Z., El-Shafie A.

    Published 2023
    “…Ant colony optimization; Hydroelectric power; Hydroelectric power plants; Investments; Particle swarm optimization (PSO); Reservoirs (water); Stream flow; Water supply; Ant colony algorithms; Hydro-power generation; Hydropower reservoirs; Optimization algorithms; Particle swarm optimization algorithm; Reservoir performance; Streamflow generations; Uncertainty and variability; Genetic algorithms…”
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  12. 12
    New heuristic function in ant colony system for the travelling salesman problem

    New heuristic function in ant colony system for the travelling salesman problem by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

    Published 2012
    “…Ant Colony System (ACS) is one of the best algorithms to solve NP-hard problems.However, ACS suffers from pheromone stagnation problem when all ants converge quickly on one sub-optimal solution.ACS algorithm utilizes the value between nodes as heuristic values to calculate the probability of choosing the next node. …”
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  13. 13
    New heuristic function in ant colony system algorithm

    New heuristic function in ant colony system algorithm by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza, Yusof, Yuhanif, Mahmuddin, Massudi, Alobaedy, Mustafa Muwafak

    Published 2012
    “…NP-hard problem can be solved by Ant Colony System (ACS) algorithm.However, ACS suffers from pheromone stagnation problem, a situation when all ants converge quickly to one sub-optimal solution.ACS algorithm utilizes the value between nodes as heuristic value to calculate the probability of choosing the next node.However, the heuristic value is not updated throughout the process to reflect new information discovered by the ants.This paper proposes a new heuristic function for the Ant Colony System algorithm that can reflect new information discovered by ants.The credibility of the new function was tested on travelling salesman and grid computing problems.Promising results were obtained when compared to classical ACS algorithm in terms of best tour length for the travelling sales-man problem. …”
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  14. 14
    Interacted multiple ant colonies optimization framework: An experimental study of the evaluation and the exploration techniques to control the search stagnation

    Interacted multiple ant colonies optimization framework: An experimental study of the evaluation and the exploration techniques to control the search stagnation by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana, Md. Norwawi, Norita

    Published 2010
    “…The performance of IMACO was demonstrated by comparing it with the best performing ant algorithms like Ant Colony System (ACS) and Max-Min Ant System (MMAS). …”
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  15. 15
    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
    “…Swarm intelligence algorithms have been applied in solving these problems including the Ant Colony System (ACS) which is one of the ant colony optimization variants. …”
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  16. 16
    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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  17. 17
    Scheduling jobs in computational grid using hybrid ACS and GA approach

    Scheduling jobs in computational grid using hybrid ACS and GA approach by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

    Published 2014
    “…Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems.However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time.This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to solve the job scheduling problem.The high level hybridization algorithm will keep the identity of each algorithm in performing the scheduling task.The study focuses on static grid computing environment and the metrics for optimization are the makespan and flowtime.Experiment results show that the proposed algorithm outperforms other stand-alone algorithms such as ant system, genetic algorithms, and ant colony system for makespan.However, for flowtime, ant system and genetic algorithm perform better.…”
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  18. 18
    Applying DACS3 in the Capacitated Vehicle Routing Problem

    Applying DACS3 in the Capacitated Vehicle Routing Problem by Md Rais, Helmi, Ali Othman, Zulaiha, Hamdan, Abdul Razak

    Published 2010
    “…Several versions of Ant Colony Optimization (ACO) algorithms have been proposed which aim to achieve an optimum solution includes Dynamic Ant Colony System with Three Level Updates (DACS3). …”
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  19. 19
    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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  20. 20
    Analysis of the stagnation behavior of the interacted multiple ant colonies optimization framework

    Analysis of the stagnation behavior of the interacted multiple ant colonies optimization framework by Aljanaby, Alaa, Ku-Mahamud, Ku Ruhana

    Published 2011
    “…Search Stagnation is a common problem that all Ant Colony Optimization (ACO) algorithms suffer from regardless of their application domain. …”
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