MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Kailasam, Jeyaganesh Kumar; Nalliah, Rajkumar; Nallagoundanpalayam Muthusamy, Saravanakumar; Manoharan, Premkumar

doi:10.3390/biomimetics8080615

Cited by 3 publications

(2 citation statements)

References 110 publications

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“…Many metaheuristic algorithms have recently been reported in addition to the above-discussed algorithms for numerical and real-world engineering design optimization problems, including data clustering. For instance, ant colony optimization 35 , firefly algorithm 36 , 37 , flower pollination algorithm 38 , grey wolf optimizer (GWO) 39 – 42 , Jaya algorithm 43 , Teaching–learning based optimization (TLBO) algorithm 44 , Rao algorithm 45 , political optimizer 46 , whale optimization algorithm (WOA) 47 , Moth flame algorithm (MFO) 48 , multi-verse optimizer (MVO) 49 , Salp swarm algorithm (SSA) 50 , 51 , spotted hyena optimizer 52 , butterfly optimization 53 , lion optimization 54 , fireworks algorithm 55 , Cuckoo search algorithm 56 , bat algorithm 57 , Tabu search 58 , harmony search algorithm 59 , Newton–Raphson optimizer 60 , reptile search algorithm 61 , slime mould algorithm 62 , 63 , harris hawk optimizer 64 , Chimp optimizer 65 , artificial gorilla troop optimizer 66 , atom search algorithm 67 , marine predator algorithm 68 , 69 , sand cat swarm algorithm 70 , equilibrium optimizer 71 , 72 , Henry gas solubility algorithm (HGSA) 73 , resistance–capacitance algorithm 74 , arithmetic optimization algorithm 75 , quantum-based avian navigation optimizer 76 , multi trail vector DE algorithm 10 , 77 , arithmetic optimization algorithm 78 , starling murmuration optimizer 79 , atomic orbit search (AOS) 80 , subtraction-average-based optimizer 81 , etc. are reported for solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

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Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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“…The performance of the SSAMGO algorithm is evaluated against SSAGWO, PSOGSA, MPSOGA, and CPSO algorithms using five benchmark functions with diverse characteristics. This comparison provides a comprehensive assessment of the effectiveness of these algorithms [47][48].…”

Section: A Validation Of Benchmark Functionsmentioning

confidence: 99%

A Hybrid Sparrow Search Optimized Fractional Virtual Inertia Control for Frequency Regulation of Multi-Microgrid System

Fadheel,

Wahab,

Manoharan

et al. 2024

IEEE Access

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This paper introduces a robust approach, integrating a Virtual Inertia Controller (VIC) with a modified demand response controller for an islanded Multi-Microgrid (MMG) system, accommodating high levels of Renewable Energy Sources (RESs). In these MGs, the low inertia in the system has an undesirable impact on the stability of MG frequency. As a result, it leads to a weakening of the MGs overall performance. A novel fractional derivative virtual inertia is integrated into the VIC loop to address this issue. This enhancement aims to fortify the MG's stability and robust performance, particularly when facing contingencies. Furthermore, a modified demand response controller has been incorporated into the proposed inertia control technique to mitigate the frequency fluctuations and reduce stress on the energy storage system (ESS). Fractional Order Proportional Integral Derivative (FOPID) controllers have been employed to regulate the active power output of the biodiesel generators and the Geothermal station in the MG. The hybrid sparrow search and mountain gazelle optimizer algorithm (SSAMGO) optimizes the parameters for the three-loop controller. Time-domain simulations assess the effectiveness of proposed controllers in enhancing system frequency stability. SSAMGO's performance was comprehensively evaluated, comparing it to various optimization algorithms in diverse scenarios. The results obtained from the MMG system demonstrate that utilizing the proposed controller technique, optimized with hybrid SSAMGO parameters, yields notable improvements in settling time by 24.68%, 46.20%, 7.52%, and 61.01%, steady-state error values by 72.56%, 98.18%, 98.73%, and 6.67%, undershoot by 105.76%, 144.23%, 19.23%, and 7.69% compared to other stateof-the-art algorithms presented in the literature. Finally, the proposed control technique's effectiveness and robustness are assessed in comparison to conventional inertia control across various system scenarios. These scenarios encompass random load demand fluctuations, real-time changes in RES, and a wide spectrum of system operations, including situations with reduced damping and inertia and high levels of load variation.INDEX TERMS Virtual inertia control, Load frequency control, Renewable energy resources, Hybrid optimization algorithm, Demand response control, Power system stability, Microgrid system.

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Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer

Sundar Ganesh,

Kumar,

Premkumar

et al. 2024

Sci Rep

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The advancement of Photovoltaic (PV) systems hinges on the precise optimization of their parameters. Among the numerous optimization techniques, the effectiveness of each often rests on their inherent parameters. This research introduces a new methodology, the Reinforcement Learning-based Golden Jackal Optimizer (RL-GJO). This approach uniquely combines reinforcement learning with the Golden Jackal Optimizer to enhance its efficiency and adaptability in handling various optimization problems. Furthermore, the research incorporates an advanced non-linear hunting strategy to optimize the algorithm’s performance. The proposed algorithm is first validated using 29 CEC2017 benchmark test functions and five engineering-constrained design problems. Secondly, rigorous testing on PV parameter estimation benchmark datasets, including the single-diode model, double-diode model, three-diode model, and a representative PV module, was carried out to highlight the superiority of RL-GJO. The results were compelling: the root mean square error values achieved by RL-GJO were markedly lower than those of the original algorithm and other prevalent optimization methods. The synergy between reinforcement learning and GJO in this approach facilitates faster convergence and improved solution quality. This integration not only improves the performance metrics but also ensures a more efficient optimization process, especially in complex PV scenarios. With an average Freidman’s rank test values of 1.564 for numerical and engineering design problems and 1.742 for parameter estimation problems, the proposed RL-GJO is performing better than the original GJO and other peers. The proposed RL-GJO stands out as a reliable tool for PV parameter estimation. By seamlessly combining reinforcement learning with the golden jackal optimizer, it sets a new benchmark in PV optimization, indicating a promising avenue for future research and applications.

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MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

Cited by 3 publications

References 110 publications

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

A Hybrid Sparrow Search Optimized Fractional Virtual Inertia Control for Frequency Regulation of Multi-Microgrid System

Enhancing photovoltaic parameter estimation: integration of non-linear hunting and reinforcement learning strategies with golden jackal optimizer

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