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Despite the good performance of Crow Search Algorithm (CSA) in dealing with global optimization problems, unfortunately it is not the case with respect to the convergence performance. Conventional CSA exploration and exploitation are strongly dependent on the proper setting of awareness probability (AP) and flight length (FL) parameters. In each optimization problem, AP and FL parameters are set in an ad hoc manner and their values do not change over the optimization process. To this date, there is no analytical approach to adjust their best values. This presents a major drawback to apply CSA in complex practical problems. Hence, the conventional CSA is used only for limited problems due to fact that CSA with fixed AP and FL is frequently trapped into local optimum. In this present paper, an enhanced version of CSA called dynamic crow search algorithm (DCSA) is proposed to overcome the drawbacks of the conventional CSA. In the proposed DCSA, two modifications of the basic algorithm are made. The first modification concerns the continuous adjustment of the CSA parameters leading to a DCSA, where AP will be adjusting linearly over optimization process and FL will be adjusting according to the generalized Pareto probability density function. This dynamic adjustment will provide more global search capability as well as more exploitation of the pre-final solutions. The second modification concerns the improvement of CSA's swarm diversity in the search process. This will lead to a high convergence accuracy, and fast convergence rate. The effectiveness of the proposed algorithm is validated using a set of experimental series using 13 complex benchmark functions. Experimental results highly proved the modified algorithm effectiveness compared to the basic algorithm in terms of convergence rate, global search capability and final solutions. In addition, a comparison with conventional and recent similar algorithms revealed that DCSA gives superior results in terms of performance and efficiency.

A revised translating bed total body irradiation (TBI) technique is developed for shielding organs at risk (lungs) to tolerance dose limits, and optimizing dose distribution in three dimensions (3D) using an asymmetrically‐adjusted, dynamic multileaf collimator. We present a dosimetric comparison of this technique with a previously developed symmetric MLC‐based TBI technique. An anthropomorphic RANDO phantom is CT scanned with 3 mm slice thickness. Radiological depths (RD) are calculated on individual CT slices along the divergent ray lines. Asymmetric MLC apertures are defined every 9 mm over the phantom length in the craniocaudal direction. Individual asymmetric MLC leaf positions are optimized based on RD values of all slices for uniform dose distributions. Dose calculations are performed in the Eclipse treatment planning system over these optimized MLC apertures. Dose uniformity along midline of the RANDO phantom is within the confidence limit (CL) of 2.1% (with a confidence probability p=0.065). The issue of over‐ and underdose at the interfaces that is observed when symmetric MLC apertures are used is reduced from more than ±4% to less than ±1.5% with asymmetric MLC apertures. Lungs are shielded by 20%, 30%, and 40% of the prescribed dose by adjusting the MLC apertures. Dose‐volume histogram analysis confirms that the revised technique provides effective lung shielding, as well as a homogeneous dose coverage to the whole body. The asymmetric technique also reduces hot and cold spots at lung‐tissue interfaces compared to previous symmetric MLC‐based TBI technique. MLC‐based shielding of OARs eliminates the need to fabricate and setup cumbersome patient‐specific physical blocks.PACS number(s): 87.55.‐x, 87.55.de, 87.55.D‐

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