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  1. 1
    A refined differential evolution algorithm for improving the performance of optimization process

    A refined differential evolution algorithm for improving the performance of optimization process by A. R., Yusoff, Nafrizuan, Mat Yahya

    Published 2011
    “…DE is developed based on an improved Genetic Algorithm and come with different strategies for faster optimization. …”
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  2. 2
    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
    “…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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  3. 3
    Comparison between Lamarckian Evolution and Baldwin Evolution of neural network

    Comparison between Lamarckian Evolution and Baldwin Evolution of neural network by Taha, Imad, Inazy, Qabas

    Published 2006
    “…We presented hybrid genetic algorithm for optimizing weights as well as the topology of artificial neural networks, by introducing the concepts of Lamarckian and Baldwin evolution effects. …”
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  4. 4
    A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection

    A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

    Published 2019
    “…Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. …”
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  5. 5
    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain

    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

    Published 2018
    “…An improved version of Differential Evolution (DE) namely Backtracking Search Algorithm (BSA) is applied to several fed batch fermentation problems and its performance is compared with recent emerging metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and DE. …”
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  6. 6
    A multidimensional data descriptor tool based on fuzzy min max neural network algorithm

    A multidimensional data descriptor tool based on fuzzy min max neural network algorithm by Fatin Nurjannah, Salauddin

    Published 2018
    “…They have been compare for their advantages and disadvantages. The implementation of Fuzzy Min Max Neural Network technique has been applied using Matlab Programming. …”
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  7. 7
    Resource allocation in coordinated multipoint long term evolution-advanced networks

    Resource allocation in coordinated multipoint long term evolution-advanced networks by Katiran, Norshidah

    Published 2015
    “…The resource allocation algorithm is developed through three phases, namely Low-Complexity Resource Allocation (LRA), Optimized Resource Allocation (ORA) and Cross-Layer Design of ORA (CLD-ORA). …”
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  8. 8
    An event-driven simulation for highest urgency first (HUF) : a latency and modulation aware bandwidth allocation algorithm for WiMAX base stations.

    An event-driven simulation for highest urgency first (HUF) : a latency and modulation aware bandwidth allocation algorithm for WiMAX base stations. by Naghshbandi, Danial, Subramaniam, Shamala, Moghadam, Saeed Rahimi

    Published 2013
    “…This research has focused at developing a discrete-event simulator to implement bandwidth allocation algorithms for a WiMAX base station.The highest urgency first algorithm has been utilized as a benchmark deployment algorithm in order to provide: dynamic downlink/uplink adjustment and latency guarantee for real-time applications. …”
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  9. 9
    Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem

    Recent Evolutionary Algorithm Variants for Combinatorial Optimization Problem by Anniza, Hamdan, San Nah, Sze, Say Leng, Goh, Kang Leng, Chiew, Wei King, Tiong

    Published 2023
    “…Other metaheuristic approaches such as genetic algorithm, differential evolution algorithm, particle swarm optimization, and ant colony optimization are still preferable to address combinatorial optimization problems. …”
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  10. 10
    Maximum 2-satisfiability in radial basis function neural network

    Maximum 2-satisfiability in radial basis function neural network by Shehab Abdulhabib Alzaeemi, Saratha Sathasivam, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor

    Published 2020
    “…The simulated results suggest that the proposed algorithm is effective in doing MAX2SAT logic programming by analysing the performance by obtaining lower Root Mean Square Error, high ratio of satisfied clauses and lesser CPU time.…”
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  11. 11
    Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization

    Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization by Qin, Feng, AZLAN, MOHD ZAIN, KAI-QING, ZHOU, Norfadzlan, Yusup, DIDIK DWI, PRASETYA, ROZITA, ABDUL JALIL, ZAHEERA, ZAINAL ABIDIN, MAHADI, BAHARI, YUSRI, KAMIN, MAZLINA, ABDUL MAJID

    Published 2025
    “…Harmony search algorithm (HSA) is extensively utilized in engineering optimization. …”
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  12. 12
    A holistic review on artificial intelligence techniques for well placement optimization problem

    A holistic review on artificial intelligence techniques for well placement optimization problem by Islam, J., Vasant, P.M., Negash, B.M., Laruccia, M.B., Myint, M., Watada, J.

    Published 2020
    “…Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. …”
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  13. 13
    Digital beamforming implementation of switch-beam smart antenna system by using integrated digital signal processor and field-programmable gate array

    Digital beamforming implementation of switch-beam smart antenna system by using integrated digital signal processor and field-programmable gate array by Vakilian, Vida

    Published 2008
    “…After modeling, the algorithm is implemented in DSP. By using the Hardware- In-Loop facility of DSP, the comparison between hardware implementation and software modeling is performed. …”
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  14. 14
    Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm

    Optimization of fed-batch fermentation processes using the Backtracking Search Algorithm by Mohd Zain, Mohamad Zihin, Kanesan, Jeevan, Kendall, Graham, Chuah, Joon Huang

    Published 2018
    “…However, the complexity of these processes requires an expert system that involves swarm intelligence-based metaheuristics such as Artificial Algae Algorithm (AAA), Artificial Bee Colony (ABC), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Differential Evolution (DE) for simulation and optimization of the feeding trajectories. …”
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  15. 15
    Optimized differential evolution algorithm for linear frequency modulation radar signal denoising

    Optimized differential evolution algorithm for linear frequency modulation radar signal denoising by Al-Dabbagh, Mohanad Dawood Hasan

    Published 2013
    “…The main contention of this thesis is to investigate the development of new optimization technique based on Differential Evolution algorithm (DE), applied for radar signal denoising application. …”
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  16. 16
    Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization

    Hybrid Harmony Search Algorithm Integrating Differential Evolution and Lévy Flight for Engineering Optimization by Qin, Feng, Mohd Zain, Azlan, Yusup, Norfadzlan, Prasetya, Didik Dwi, Abdul Jalil, Rozita, Bahari, Mahadi, Kamin, Yusri, Abdul Majid, Mazlina

    Published 2025
    “…Harmony search algorithm (HSA) is extensively utilized in engineering optimization. …”
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  17. 17
    Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game

    Automatic generation of neural game controller using single and bi-objective evolutionary optimization algorithms for RTS Game by Chang, Kee Tong

    Published 2015
    “…The proposed EC methods are Genetic Algorithm (GA), Differential Evolution (DE), Evolutionary Programming (EP), and Pareto-based Differential Evolution (PDE). …”
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  18. 18
    Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking

    Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking by Shen, Jiazheng, Hong, Tang Sai, Fan, Luxin, Zhao, Ruixin, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan

    Published 2024
    “…The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). …”
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  19. 19
    Hybrid harmony search algorithm integrating differential evolution and lévy flight for engineering optimization

    Hybrid harmony search algorithm integrating differential evolution and lévy flight for engineering optimization by Qin, Feng, Azlan, Mohd Zain, Zhou, Kaiqing, Norfadzlan, Yusup, Dwi Prasetya, Didik, Rozita, Abdul Jalil, Zaheera, Zainal Abidin, Mahadi, Bahari, Yusri, Kamin, Mazlina, Abdul Majid

    Published 2025
    “…Harmony search algorithm (HSA) is extensively utilized in engineering optimization. …”
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  20. 20
    A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence

    A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence by Tse, Guan Tan, Teo, Jason Tze Wi, Rayner Alfred, Kim, On Chin

    Published 2013
    “…The Pareto Archived Evolution Strategy (PAES) algorithm is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing game scores and minimizing neural network complexity. …”
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