Search Results - optimal ((step algorithm) OR (search algorithm))

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  1. 1

    Analysis of the effect of search step size on the accuracy and convergence properties of electromagnetism-like mechanism algorithm by Tan J.D., Dahari M., Koh S.P., Koay Y.Y., Abed I.A.

    Published 2023
    “…Benchmarking; Global optimization; Convergence performance; Convergence properties; Electromagnetism-like mechanism algorithms; Global optimization problems; Meta-heuristic search algorithms; Metaheuristic; Modified algorithms; Search steps; Optimization…”
    Article
  2. 2

    An adaptive gravitational search algorithm for global optimization by Koay Y.-Y., Tan J.-D., Lim C.-W., Koh S.-P., Tiong S.-K., Ali K.

    Published 2023
    “…Gravitational Search Algorithm (GSA) is one of the relatively new population-based optimization algorithms. …”
    Article
  3. 3

    An improved electromagnetism-like mechanism algorithm for the optimization of maximum power point tracking / Tan Jian Ding by Tan, Jian Ding

    Published 2017
    “…The SPCEM applied a dynamic strategy to regulate the search steps during the local search. The search scheme began with relatively bigger steps. …”
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  4. 4
  5. 5

    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

    Published 2021
    “…This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. …”
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    Article
  6. 6

    An improved bat algorithm with artificial neural networks for classification problems by Rehman Gillani, Syed Muhammad Zubair

    Published 2016
    “…Metaheuristic search algorithms have been used for quite a while to optimally solve complex searching problems with ease. …”
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  7. 7

    Enhancing harmony search parameters based on step and linear function for bus driver scheduling and rostering problems by Mansor, Nur Farraliza

    Published 2018
    “…Optimization is a major challenge in numerous practical world problems.According to the “No Free Lunch (NFL)” theorem,there is no existing single optimizer algorithm that is able to resolve all issues in an effective and efficient manner.It is varied and need to be solved according to the specific capabilities inherent to certain algorithms making it hard to foresee the algorithm that is best suited for each problem.As a result,the heuristic technique is adopted for this research as it has been identified as a potentially suitable algorithm.Alternative heuristic algorithms are also suggested to obtain optimal solutions with reasonable computational effort.However,the heuristic approach failed to produce a solution that nears optimum when the complexity of a problem increases;therefore a type of nature-inspired algorithm known as meta-euristics which utilises an intelligent searching mechanism over a population is considered and consequently used.The meta-heuristic approach is widely used to substitute heuristic terms and is broadly applied to address problems with regards to driver scheduling.However,this meta-heuristic technique is still unable to address the fairness issue in the scheduling and rostering problems.Hence,this research proposes a strategy to adopt an amendment of the harmony search algorithm in order to address the fairness issue which in turn will escalate the level of fairness in driver scheduling and rostering.The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems.In this respect,the three main operators in harmony search,namely the Harmony Memory Consideration Rate (HMCR),Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration.These parameters influence the overall performance of the HS algorithm,and therefore it is crucial to fine-tune them. …”
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  8. 8

    A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting by Mohammed, Athraa Jasim, Ghathwan, Khalil Ibrahim, Yusof, Yuhanis

    Published 2020
    “…Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. …”
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    Article
  9. 9

    Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

    Published 2014
    “…In the proposed HACPSO algorithm, initially accelerated particle swarm optimization (APSO) algorithm searches within the search space and finds the best sub-search space, and then the CS selects the best nest by traversing the sub-search space. …”
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  10. 10
  11. 11

    Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

    Published 2020
    “…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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  12. 12

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

    Published 2019
    “…The proposed algorithm simulates the behavior of the nomads when they are searching for life sources (water or grazing fields). …”
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  13. 13

    Opposition-based spiral dynamic algorithm with an application to optimize type-2 fuzzy control for an inverted pendulum system by Nasir, Ahmad Nor Kasruddin, Abdul Razak, Ahmad Azwan

    Published 2022
    “…They are also applied to optimize a type-2 fuzzy logic controller for an inverted pendulum system in comparison to SDA, Spotted Hyena Optimizer, Tunicate Swarm Algorithm, and Sooty Tern Optimization Algorithm. …”
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  14. 14

    Using artificial intelligence search in solving the camera placement problem by Altahir, A.A., Asirvadam, V.S., Hamid, N.H.B., Sebastian, P.

    Published 2022
    “…The chapter also carries out an analytical review of three main searching algorithms namely, generate and test, uninformed search, and hill climbing search algorithms. …”
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    Book
  15. 15

    Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm by Hossain Lipu M.S., Hannan M.A., Hussain A., Ansari S., Rahman S.A., Saad M.H.M., Muttaqi K.M.

    Published 2024
    “…Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. …”
    Article
  16. 16

    Ideal combination feature selection model for classification problem based on bio-inspired approach by Basir, Mohammad Aizat, Hussin, Mohamed Saifullah, Yusof, Yuhanis

    Published 2020
    “…The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. …”
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    Book Section
  17. 17

    A New And Fast Rival Genetic Algorithm For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim

    Published 2021
    “…Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. …”
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  18. 18

    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
    “…As a meta-heuristic algorithm, Harmony Search (HS) algorithm is a population-based meta-heuristics approach that is superior in solving diversified large scale optimization problems. …”
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    Conference or Workshop Item
  19. 19

    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
    “…As a meta-heuristic algorithm, Harmony Search (HS) algorithm is a population-based meta-heuristics approach that is superior in solving diversified large scale optimization problems. …”
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  20. 20

    An Improved Spiral Dynamic Optimization Algorithm with Engineering Application by Ahmad Nor Kasruddin, Nasir, Tokhi, M. O.

    Published 2015
    “…However, the algorithm is subjected to settling into local optima at the end of the search process, due to insufficient exploration throughout the search area. …”
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    Article