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  1. 1
    Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning

    Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning by Alomoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Aloufi, Khalid, Kamal Z., Zamli

    Published 2019
    “…Then, a modified version of opposition-based learning technique has been applied on the hybrid algorithm to improve the HS exploration because HS easily gets trapped into local optima. …”
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  2. 2
    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING

    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…The solution set (i.e. optimized weight/bias matrix of ANN) provided by the optimized and improved genetic algorithm and modified BP based model is extracted and used in the design and development of a prototype device of the proposed model. …”
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  3. 3
    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
    “…Opposition-based learning variants enhanced the explorations and help optimization algorithms to avoid local optima falling. …”
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  4. 4
    A modified crow search algorithm with niching technique for numerical optimization

    A modified crow search algorithm with niching technique for numerical optimization by Islam, J., Vasant, P.M., Negash, B.M., Watada, J.

    Published 2019
    “…Despite its easy implementation, crow search algorithm has weakness to find global optima and suffers from slow convergence rate in multi-modal optimization problems. …”
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  5. 5
    A modified crow search algorithm with niching technique for numerical optimization

    A modified crow search algorithm with niching technique for numerical optimization by Islam, J., Vasant, P.M., Negash, B.M., Watada, J.

    Published 2019
    “…Despite its easy implementation, crow search algorithm has weakness to find global optima and suffers from slow convergence rate in multi-modal optimization problems. …”
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  6. 6
    A modified niching crow search approach to well placement optimization

    A modified niching crow search approach to well placement optimization by Islam, J., Rahaman, M.S.A., Vasant, P.M., Negash, B.M., Hoqe, A., Alhitmi, H.K., Watada, J.

    Published 2021
    “…In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. …”
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  7. 7
    An ensemble of neural network and modified grey wolf optimizer for stock prediction

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…Widespread models like Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Evolutionary Strategy (ES) and Population-Based Incremental Learning (PBIL) dealing with the specified problems are also explored and compared. …”
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  8. 8
    Formulation of metaheuristic algorithms based on artificial bee colony for engineering problems

    Formulation of metaheuristic algorithms based on artificial bee colony for engineering problems by Lee, Wei Wen

    Published 2024
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  9. 9
    Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization

    Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization by Yap, David F. W., Koh, S. P., Tiong, S. K.

    Published 2011
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  10. 10
    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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  11. 11
    Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

    Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm by Altrad, Abdallah Mahmoud Mousa

    Published 2018
    “…The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. …”
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  12. 12
    QTCP: an optimized and improved congestion control algorithm of high-speed TCP networks

    QTCP: an optimized and improved congestion control algorithm of high-speed TCP networks by Qureshi, Barkatullah, Othman, Mohamed, K. Subramaniam, Shamala, Abdul Hamid, Nor Asilah Wati

    Published 2011
    “…To overcome these problems Quick Transport Control Protocol (QTCP) algorithm based on optimizations of HS-TCP slow start algorithm and Additive Increase and Multiplicative Decrease (AIMD) algorithm have been proposed. …”
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  13. 13
    A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot

    A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot by Low, Ee Soong, Ong, Pauline, Low, Cheng Yee

    Published 2023
    “…Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. …”
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  14. 14
    A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot

    A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot by Ee Soong Low, Ee Soong Low, Pauline Ong, Pauline Ong, Cheng Yee Low, Cheng Yee Low

    Published 2023
    “…Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. …”
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  15. 15
    Development of optimization Alghorithm for uncertain non-linear dynamical system

    Development of optimization Alghorithm for uncertain non-linear dynamical system by Abdul Aziz, Mohd. Ismail, Yaacob, Nazeeruddin, Mohd. Said, Norfarizan, Hamzah, Nor Hazadura

    Published 2004
    “…Based on the results of these simulations, we compared the number of iterations needed by each algorithm to arrive at the optimal solution and the CPU time taken for each algorithm to execute the search. …”
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  16. 16
    A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot

    A modified Q-learning path planning approach using distortion concept and optimization in dynamic environment for autonomous mobile robot by Ee Soong Low, Ee Soong Low, Pauline Ong, Pauline Ong, Cheng Yee Low, Cheng Yee Low

    Published 2023
    “…Comparative analyses are performed based on several metrics, including computational time, travelled distance, collision rate, and success rate. …”
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  17. 17
    Fuzzy adaptive emperor penguin optimizer for global optimization problems

    Fuzzy adaptive emperor penguin optimizer for global optimization problems by Md Abdul, Kader

    Published 2023
    “…The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. …”
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  18. 18
    Memoryless modified symmetric rank-one method for large-scale unconstrained optimization

    Memoryless modified symmetric rank-one method for large-scale unconstrained optimization by Modarres, Farzin, Abu Hassan, Malik, Leong, Wah June

    Published 2009
    “…In this study, we present a scaled memoryless modified Symmetric Rank-One (SR1) algorithm and investigate the numerical performance of the proposed algorithm for solving large-scale unconstrained optimization problems. …”
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  19. 19
    Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods

    Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods by Mohammed Ridha, Hussein, Hizam, Hashim, Mirjalili, Seyedali, Othman, Mohammad Lutfi, Ya'acob, Mohammad Effendy, Ahmadipour, Masoud

    Published 2022
    “…The proposed guaranteed convergence arithmetic optimization algorithm based on efficient modified third order Newton Raphson (GCAOAEmNR) method highlights important contributions to the literature in terms of methodology (explorer-exploiter phases) and objective function design. …”
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  20. 20
    Broadening selection competitive constraint handling algorithm for faster convergence

    Broadening selection competitive constraint handling algorithm for faster convergence by Shaikh, T.A., Hussain, S.S., Tanweer, M.R., Hashmani, M.A.

    Published 2020
    “…The mean closure performance of the BSCCH algorithm is compared against seven selected state-of-the-art algorithms, namely Differential Evolution with Adaptive Trial Vector Generation Strategy and Cluster-replacement-based Feasibility Rule (CACDE), Improved Teaching Learning Based Optimization (ITLBO), Modified Global Best Artificial Bee Colony (MGABC), Stochastic Ranking Differential Evolution (SRDE), Novel Differential Evolution (NDE), Partical Swarm Optimization for solving engineering problems-a new constraint handling mechanism (CVI-PSO) and Ensemble of Constraint Handling Techniques (ECHT). …”
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