Optimal Placement of Near Ground VHF/UHF Radio Communication Network as a Multi Objective Problem

Shehadeh, Hisham A.; Idris, Mohd Yamani Idna; Ahmedy, Ismail; Hassen, Hani Ragab

doi:10.1007/s11277-019-06780-6

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…The benchmarks models in this study may have their limitations, which other metrics in real experiments may affect the outcomes of the studies. Hence, in the future, the proposed HGASSO should be implemented to solve real problems that utilize cosine, sine, and exponent in there mathematical modeling such as minimizing propagation loss and interference of wireless, telecommunication and mobile networks (Shehadeh et al, 2020;Hamdan et al, 2015;Hamdan, 2018), engineering applications (Jayabarathi et al, 2018), designing the optimal spacing and length of Yagi-Uda antenna (Baumgartner et al, 2017). Furthermore, in the future, the proposed HGASSO should be compared against a set of well-known approaches, such as "Chaotic Gravitational Search Algorithm (CGSA)" (Rather & Bala, 2020), "Grey Wolf Optimizer (GWO)" (Mirjalili et al, 2014), and "Sine Cosine Algorithm (SCA)" (Mirjalili et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Shehadeh

Mustafa

Tubishat

2022

International Journal of Applied Metaheuristic Computing

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In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) operates based on incorporates concepts from GA and SSO in which generates individuals in a new iteration not only by crossover and mutation operations as proposed in GA, but also by techniques of local search of SSO. The main idea behind this hybridization is to reduce the probability of trapping in local optimum of multi modal problem. Our algorithm is compared against GA, and SSO metaheuristic optimization algorithms. The experimental results using a suite of multimodal benchmarks functions taken from the literature have evinced the superiority of the proposed HGASSO approach over the other approaches in terms of quality of results and convergence rates in which obtained good results in solving the multimodal benchmarks functions that include cosine, sine, and exponent in their formulation.

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Section: Discussionmentioning

confidence: 99%

A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Shehadeh

Mustafa

Tubishat

2022

International Journal of Applied Metaheuristic Computing

Self Cite

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“…They concluded that thicker leaves consist of more moisture reducing the permittivity of the transmitted signal. Shehadeh et al (2019) have highlighted the strong impact of the foliage and tree trunk height on the received signal quality for the near ground link. Their three-optimization algorithm results indicated a sharp increase in path loss within a 2-km foliage depth and showed stability with a further increase in the foliage depth.…”

Section: Forest Attenuation Modelsmentioning

confidence: 99%