Search Results - (( exploring local optimization algorithm ) OR ( java simulation optimization algorithm ))

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  1. 1
    Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness

    Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness by Noori, Mustafa Sabah, Sahbudin, Ratna K.Z., Sali, Aduwati, Hashim, Fazirulhisyam

    Published 2024
    “…One of these MOO algorithms Multi-Objective Particle Swarm Optimization (MOPSO) extends it to handle problems with multiple objectives simultaneously, but like many swarm-based algorithms, MOPSO can suffer from premature convergence or local optima solutions. …”
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  2. 2
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…The simulation is implemented with iFogSim and java programming language. …”
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  3. 3
    Improved flower pollination optimization algorithm based on swap operator and dynamic switch probability selection

    Improved flower pollination optimization algorithm based on swap operator and dynamic switch probability selection by Iqbal, Muhammad

    Published 2023
    “…Meta-heuristic algorithms have become popular in finding optimal solutions for nonlinear complex problems. …”
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  4. 4
    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems

    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

    Published 2022
    “…To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. …”
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  5. 5
    The effect of job satisfaction on the relationship between organizational culture and organizational performance

    The effect of job satisfaction on the relationship between organizational culture and organizational performance by Imran, Muhammad

    Published 2023
    “…Meta-heuristic algorithms have become popular in finding optimal solutions for nonlinear complex problems. …”
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  6. 6
    Hybridizing Invasive Weed Optimization with Firefly Algorithm for Unconstrained and Constrained Optimization Problems

    Hybridizing Invasive Weed Optimization with Firefly Algorithm for Unconstrained and Constrained Optimization Problems by Hyreil A., Kasdirin, N. M., Yahya, M. S. M., Aras, Tokhi, M. O.

    Published 2017
    “…The invasive weed optimization (IWO) algorithm, on the other hand, is effective in accurate global search, but not in local search. …”
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  7. 7
    Reactive max-min ant system: An experimental analysis of the combination with K-OPT local searches

    Reactive max-min ant system: An experimental analysis of the combination with K-OPT local searches by Sagban, Rafid, Ku-Mahamud, Ku Ruhana, Abu Bakar, Muhamad Shahbani

    Published 2015
    “…The exploration versus exploitation dilemma rises in ACO search.Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation.It memorizes the search regions in terms of reactive heuristics to be harnessed after restart, which is to avoid the arbitrary exploration later.This paper examined the assumption that local heuristics are useless when combined with local search especially when it applied for combinatorial optimization problems with rugged fitness landscape.Results showed that coupling reactive heuristics with k-Opt local search algorithms produces higher quality solutions and more robust search than max-min ant system algorithm.Well-known combinatorial optimization problems are used in experiments, i.e. traveling salesman and quadratic assignment problems. …”
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  8. 8
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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  9. 9
    An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization

    An Adaptive Switching Cooperative Source Searching And Tracing Algorithms For Underwater Acoustic Source Localization by Majid, Mad Helmi Ab.

    Published 2019
    “…In order to optimize search space exploration and to maintain inter-robot communication connectivity at swarm level, a dispersion algorithm based on attraction and repulsion force is proposed. …”
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  10. 10
    Modified Opposition Based Learning to Improve Harmony Search Variants Exploration

    Modified Opposition Based Learning to Improve Harmony Search Variants Exploration by Al-Omoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Kamal Z., Zamli

    Published 2020
    “…HS such as many optimization algorithms suffers from a weak exploration and susceptible to fall in local optima. …”
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  11. 11
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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  12. 12
    A new HMCR parameter of harmony search for better exploration

    A new HMCR parameter of harmony search for better exploration by Mansor, N.F., Abas, Z.A, Rahman, A.F.N.A, Shibghatullah, A.S., Sidek, S.

    Published 2016
    “…However, when performing a local search, the harmony search algorithm can be easily trapped in the local optima. …”
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  13. 13
    A new HMCR parameter of harmony search for better exploration

    A new HMCR parameter of harmony search for better exploration by Nur Farraliza, Mansor, Abas, Z.A, Rahman, A.F.N.A, Shibghatullah, A.S., Sidek, S

    Published 2015
    “…However, when performing a local search, the harmony search algorithm can be easily trapped in the local optima. …”
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  14. 14
    An experimental study of modified black hole algorithms

    An experimental study of modified black hole algorithms by Mohammed, Suad Khairi

    Published 2018
    “…Those algorithms are Black Hole White Hole Algorithm, Gravitational Black Hole Algorithm, Gravitational Black Hole White Hole Algorithm, Black Hole Local Search Algorithm, Black Hole White Hole Local Search Algorithm, Gravitational Black Hole Local Search Algorithm, and Gravitational Black Hole White Hole Local Search Algorithm. …”
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  15. 15
    Opposition-based Whale Optimization Algorithm

    Opposition-based Whale Optimization Algorithm by Alamri, Hammoudeh S., Alsariera, Yazan A., Kamal Z., Zamli

    Published 2018
    “…The Whale Optimization Algorithm (WOA) is a newly proposed metaheuristic optimization algorithm, which simulate humpback whales hunting behavior. …”
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  16. 16
    Multi-Swarm bat algorithm

    Multi-Swarm bat algorithm by Taha A.M., Chen S.-D., Mustapha A.

    Published 2023
    “…The exploration ability was improved by having a number of sub-swarms watching over the best local optima. …”
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  17. 17
    A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems

    A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems by Shehab, Mohammad, Khader, Ahamad Tajudin, Laouchedi, Makhlouf

    Published 2018
    “…However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithm and Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. …”
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  18. 18
    Nomadic people optimizer (NPO) for large-scale optimization problems

    Nomadic people optimizer (NPO) for large-scale optimization problems by Mohamd Salih, Sinan Qahtan

    Published 2019
    “…Three major problems are encountered when designing metaheuristics; the first problem is balancing exploration with exploitation capabilities (which leads to premature convergence or trapping in the local minima), while the second problem is the dependency of the algorithm on the controlling parameters, which are parameters with unknown optimal values. …”
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  19. 19
    Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking

    Midrange exploration exploitation searching particle swarm optimization with HSV-template matching for crowded environment object tracking by Nurul Izzatie Husna, Muhamad Fauzi

    Published 2023
    “…In this algorithm, the worst particle will be relocating to a new position to ensure the concept of exploration and exploitation remains in the search space. …”
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  20. 20
    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
    “…Local pheromone update is performed to encourage exploration to other potential sensor nodes while global pheromone update is applied to encourage the exploitation of optimal sensor nodes. …”
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