Radiation application in medicine offers extraordinary benefits. But radiation is like a double-edged sword, it has both benefits and associated risks on the community in contact. To justify the safety of workers and members of the public, regulated use of radiation is assessed by the radiation protection protocols. The aim of this study is to design a Computed Tomography (CT) facility with a simplified model of CT scanner, whose shielding follows the guidelines of National Council on Radiation Protection and Measurements (NCRP) Report No. 147. To design the study model, Monte Carlo (MC) radiation transport code in MCNPX 2.6.0 was used for the simulation. Furthermore, MCNPX was used to measure the photon flux in a vicinity or the detector cell. To validate the functioning of the X-ray tube, the experimental results were compared with the X-ray Transition Energies Database of National Institute of Standards and Technology, U.S. Department of Commerce. The results obtained were within 0.60% of relative error. To confirm the functioning of shielding design, radiation protection quantity, air kerma was measured at several points outside, and inside of the CT room and they were under the radiation dose recommended by NCRP, which demonstrates that the shielding design wassuccessful in blocking the radiation. The study can be used for an easy evaluation of any CT room within the framework of the model of the study.

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Shuffled frog leaping algorithm (SFLA) is an ongoing expansion to the group of evolutionary algorithm that imitates the societal and natural conduct of species. Upsides of particle swarm optimization (PSO) and shuffled complex evolution (SCE) is consolidates in SFLA i.e. local searching and information shuffling respectively. In this paper SFLA is improved to solve equality and inequality based constraint engineering design problems using penalty function. In proposed approach linear decreasing function that is adaptive in nature will be utilized to improve worst frog position for better exploration and convergence speed. The simulation results designate the superiority of present study over SFLA in term of global optimum solution and fast convergence rate

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Differential Evolutionary (DE) Algorithms is one of the most popular metaheuristic approach. For optimization purpose DE is very useful to solve various kind of problems. In addition to that the paper offers a Cross-Opposition Based Differential Evolution (CODE). An impression of Opposition-based learning (OBL) is incorporated in population initialization phase and in step of crossover. The performance of algorithm is analysed for different mutation strategies of DE and various other existing approaches.Results demonstrated that the algorithm outperform in terms of convergence speed, versatile population and dimension size.