Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Shehab, Mohammad; Abualigah, Laith

doi:10.21203/rs.3.rs-277857/v1

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…As shown above, each one of the RPs is specialized in a certain area to increase the validation of RLOHHO. Moreover, five metaheuristic algorithms (CSA [33], HS [34], DE [35], MFO [36], and BA [32]) are used for comparing their results with the results of RLOHHO. More details are shown in Table 9.…”

Section: Part 3: Real-world Problems Cec 2011mentioning

confidence: 99%

Improved Harris Hawks Optimization Algorithm for Solving Real Engineering Optimization Problems

Daoud

Shehab

Abualigah

et al. 2023

Preprint

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Harris Hawks Optimization (HHO) algorithm was proposed recently under the metaheuristic algorithms, which can fix many problems in various domains. However, it needs to improve in local search, which may lead to a loss of diversity, stuck in a local minimum, which procures premature convergence. Two steps have been introduced in this paper to avoid these issues. Firstly, integrating Opposition-based learning (OBL) with HHO accelerates the search process and enhances the choice of better solutions, namely OHHO. Then, using reinforcement learning to enhance the research technique of OHHO, called RLOHHO. CEC 2015 and CEC 2017 benchmark functions and real engineering problems are utilized to evaluate the efficiency. Finally, the proposed versions of HHO are compared with efficient optimization algorithms. The experiment results illustrate that the RLOHHO's version achieved better solutions than the original HHO, OHHO, and other similar published algorithms in the literature.

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Section: Part 3: Real-world Problems Cec 2011mentioning

confidence: 99%

Improved Harris Hawks Optimization Algorithm for Solving Real Engineering Optimization Problems

Daoud

Shehab

Abualigah

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Photovoltaic systems [73] RLGBO Using random learning strategy to enhance the accuracy of selected solutions of GBO Electrical energy [54] Hybridization ERVFL-GBO Using ERVFL-GBO for modeling ultrasonic welding of polymers Ultrasonic welding [58] CGBO Merge the efficiency of CLS with GBO to improve exploration mechanism…”

Section: Binarymentioning

confidence: 99%

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Daoud

Shehab

Al-Mimi

et al. 2022

Arch Computat Methods Eng

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This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization

Jerine

2024

WEB

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Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.

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Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Cited by 3 publications

References 60 publications

Improved Harris Hawks Optimization Algorithm for Solving Real Engineering Optimization Problems

Improved Harris Hawks Optimization Algorithm for Solving Real Engineering Optimization Problems

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization

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