Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

Al-Tashi, Qasem; Shami, Tareq M.; Abdulkadir, Said Jadid; Akhir, Emelia Akashah Patah; Alwadain, Ayed; Alhussain, Hitham; Alqushaibi, Alawi; Rais, Helmi Md; Muneer, Amgad; Saad, Maliazurina; Jia, Wei; Mirjalili, Seyedali

doi:10.32604/csse.2023.039788

Computer Systems Science and Engineering

2023

DOI: 10.32604/csse.2023.039788

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Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

Qasem Al-Tashi¹,

Tareq M. Shami²,

Said Jadid Abdulkadir³

et al.

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Cited by 4 publications

References 70 publications

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BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

Khodadadi,

Al-Tashi

et al. 2023

IEEE Access

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The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic that uses the main arithmetic operators' distribution behavior, such as multiplication, division, subtraction, and addition. This paper proposes a binary version of the Arithmetic Optimization Algorithm (BAOA) to tackle the feature selection problem in classification. The algorithm's search space is converted from a continuous to a binary one using the sigmoid transfer function to meet the nature of the feature selection task. The classifier uses a method known as the wrapper-based approach K-Nearest Neighbors (KNN), to find the best possible solutions. This study uses 18 benchmark datasets from the University of California, Irvine (UCI) repository to evaluate the suggested binary algorithm's performance. The results demonstrate that BAOA outperformed the Binary Dragonfly Algorithm (BDF), Binary Particle Swarm Optimization (BPSO), Binary Genetic Algorithm (BGA), and Binary Cat Swarm Optimization (BCAT) when various performance metrics were used, including classification accuracy, selected features as well as the best and worst optimum fitness values.

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BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

Khodadadi,

Al-Tashi

et al. 2023

IEEE Access

Self Cite

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Enhanced Deep Deterministic Policy Gradient Algorithm Using Grey Wolf Optimizer for Continuous Control Tasks

Sumiea,

Abdulkadir,

Ragab

et al. 2023

IEEE Access

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Deep Reinforcement Learning (DRL) allows agents to make decisions in a specific environment based on a reward function, without prior knowledge. Adapting hyperparameters significantly impacts the learning process and time. Precise estimation of hyperparameters during DRL training poses a major challenge. To tackle this problem, this study utilizes Grey Wolf Optimization (GWO), a metaheuristic algorithm, to optimize the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm for achieving optimal control strategy in two simulated Gymnasium environments provided by OpenAI. The ability to adapt hyperparameters accurately contributes to faster convergence and enhanced learning, ultimately leading to more efficient control strategies. The proposed DDPG-GWO algorithm is evaluated in the 2DRobot and MountainCarContinuous simulation environments, chosen for their ease of implementation. Our experimental results reveal that optimizing the hyperparameters of the DDPG using the GWO algorithm in the Gymnasium environments maximizes the total rewards during testing episodes while ensuring the stability of the learning policy. This is evident in comparing our proposed DDPG-GWO agent with optimized hyperparameters and the original DDPG. In the 2DRobot environment, the original DDPG had rewards ranging from -150 to -50, whereas, in the proposed DDPG-GWO, they ranged from -100 to 100 with a running average between 1 and 800 across 892 episodes. In the MountainCarContinuous environment, the original DDPG struggled with negative rewards, while the proposed DDPG-GWO achieved rewards between 20 and 80 over 218 episodes with a total of 490 timesteps.

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Optimal truss design with MOHO: A multi-objective optimization perspective

Mashru,

Tejani,

Patel

et al. 2024

PLoS ONE

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This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The Hippopotamus Optimizer (HO) is a novel approach in meta-heuristic methodology that draws inspiration from the natural behaviour of hippos. The HO is built upon a trinary-phase model that incorporates mathematical representations of crucial aspects of Hippo’s behaviour, including their movements in aquatic environments, defense mechanisms against predators, and avoidance strategies. This conceptual framework forms the basis for developing the multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions and size constraints concerning stresses on individual sections and constituent parts, these problems also involved competing objectives, such as reducing the weight of the structure and the maximum nodal displacement. The findings of six popular optimization methods were used to compare the results. Four industry-standard performance measures were used for this comparison and qualitative examination of the finest Pareto-front plots generated by each algorithm. The average values obtained by the Friedman rank test and comparison analysis unequivocally showed that MOHO outperformed other methods in resolving significant structure optimization problems quickly. In addition to finding and preserving more Pareto-optimal sets, the recommended algorithm produced excellent convergence and variance in the objective and decision fields. MOHO demonstrated its potential for navigating competing objectives through diversity analysis. Additionally, the swarm plots effectively visualize MOHO’s solution distribution of MOHO across iterations, highlighting its superior convergence behaviour. Consequently, MOHO exhibits promise as a valuable method for tackling complex multi-objective structure optimization issues.

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Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

Cited by 4 publications

References 70 publications

BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

Enhanced Deep Deterministic Policy Gradient Algorithm Using Grey Wolf Optimizer for Continuous Control Tasks

Optimal truss design with MOHO: A multi-objective optimization perspective

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