Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

Balakrishnan, K.; Dhanalakshmi, R.; Khaire, Utkarsh Mahadeo

doi:10.1002/cpe.6630

Cited by 7 publications

(2 citation statements)

References 59 publications

(55 reference statements)

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“…The better solution is then selected as the initial solution, followed by using other methods for position updates. It is important to note that, to reduce computational cost, during each iteration, each individual is evaluated [42]. If rand < t T max (where t represents the iteration count and T max represents the maximum iteration count), the random reversal learning strategy is executed.…”

Section: Random Backward Learning Strategymentioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

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The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.

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Section: Random Backward Learning Strategymentioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

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“…A modified binary version of the MPA for solving the FS problems was introduced in [ 34 ]. In their algorithm, the opposition-based learning concept was incorporated with the MPS to enhance the exploration ability of the algorithm.…”

Section: Variants Of Marine Predators Algorithmmentioning

confidence: 99%

Marine Predators Algorithm: A Review

Al-Betar

Awadallah

Makhadmeh

et al. 2023

Arch Computat Methods Eng

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Marine Predators Algorithm (MPA) is a recent nature-inspired optimizer stemmed from widespread foraging mechanisms based on Lévy and Brownian movements in ocean predators. Due to its superb features, such as derivative-free, parameter-less, easy-to-use, flexible, and simplicity, MPA is quickly evolved for a wide range of optimization problems in a short period. Therefore, its impressive characteristics inspire this review to analyze and discuss the primary MPA research studies established. In this review paper, the growth of the MPA is analyzed based on 102 research papers to show its powerful performance. The MPA inspirations and its theoretical concepts are also illustrated, focusing on its convergence behaviour. Thereafter, the MPA versions suggested improving the MPA behaviour on connecting the search space shape of real-world optimization problems are analyzed. A plethora and diverse optimization applications have been addressed, relying on MPA as the main solver, which is also described and organized. In addition, a critical discussion about the convergence behaviour and the main limitation of MPA is given. The review is end-up highlighting the main findings of this survey and suggests some possible MPA-related improvements and extensions that can be carried out in the future.

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Improved aquila optimizer with mRMR for feature selection of high-dimensional gene expression data

Qin,

Zhang,

Dong

et al. 2024

Cluster Comput

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Excogitating marine predators algorithm based on random opposition‐based learning for feature selection

Cited by 7 publications

References 59 publications

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Marine Predators Algorithm: A Review

Improved aquila optimizer with mRMR for feature selection of high-dimensional gene expression data

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