R Badlishah Ahmad
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Preferred name
R Badlishah Ahmad
Official Name
R Badlishah, Ahmad
Alternative Name
Ahmad, R. Badlishah
Ahmad, B.
Badlishah Ahmad, R.
Ahmad, Rashidi
Ahmad, Badlisha
Ahmed, R. Badlishah
Ahmad, R. B.
Badlishah, R.
Ahmad, R. Badli
Ahmed, Rbadlishah
Main Affiliation
Scopus Author ID
57194844651
Researcher ID
U-3211-2019

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Green Nanocomposite-Based Metamaterial Electromagnetic Absorbers: Potential, Current Developments and Future Perspectives

2020-01-01 , Yah N.F.N. , Rahim H.A. , Soh Ping Jack , Abdulmalek M. , R Badlishah Ahmad , Muzammil Jusoh , Seng L.Y. , Jamaluddin M.H. , Mohd Najib Mohd Yasin

The use of the natural materials instead of conventional materials as electromagnetic absorbers promotes environmental sustainability, cost-effectiveness, and ease of accessibility. Furthermore, these materials may also be designed as absorbers and as reinforcements in building materials in a lightweight form. The absorbing ability of composite materials can be customized based on the chosen fillers. Specifically, magnetic and dielectric fillers can be incorporated to improve the absorption of a composite material compared to traditional materials. This work aims to review recent developments of electromagnetic absorbers enabled by nanocomposites, metamaterial and metasurface-based, as well as green composite alternatives. First, the background concepts of electromagnetic wave absorption and reflection will be presented, followed by the assessment techniques in determining electromagnetic properties of absorbing materials. Next, the state-of-the-art absorbers utilizing different materials will be presented and their performances are compared. This review concludes with a special focus on the future perspective of the potential of metamaterial based nanocellulose composites as ultrathin and broadband electromagnetic absorbers.

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A hybrid modified method of the sine cosine algorithm using latin hypercube sampling with the cuckoo search algorithm for optimization problems

2020-11-01 , Rosli S.J. , Hasliza A Rahim @ Samsuddin , Khairul Najmy Abdul Rani , Ruzelita Ngadiran , R Badlishah Ahmad , Yahaya N.Z. , Abdulmalek M. , Muzammil Jusoh , Mohd Najib Mohd Yasin , Thennarasan Sabapathy , Allan Melvin Andrew

The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.

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