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In this paper, we propose an improved Harris hawk optimizer (IHHO) to overcome the shortcomings of Harris hawk optimizer (HHO) that there is aimless search in the global search process. IHHO is different from HHO in that during the global search process, the position of the Harris hawks is not random but moves according to the position of the best several Harris hawks in the population. The advantage of the new global search process in the IHHO is that it effectively improves the quality of candidate solutions. We use the proposed algorithm to solve job-shop scheduling problem, which is an NP-hard problem and also a very challenging optimization problem, because it is difficult to find a reasonable candidate solution in polynomial time. Many researchers have tried various methods to solve it, but failed to get an effective result. Therefore, we use it to verify the performance of the proposed algorithm. The experimental results of IHHO are compared with those of improved genetic algorithm, differential evolution and HHO. It can be seen from the experimental results that IHHO has obvious advantages compared with the results of other comparison algorithms in terms of convergence accuracy, convergence speed, stability and running time. The experimental results of Wilcoxon's rank sum test also show that the IHHO is essentially different from other comparison algorithms, which shows that the optimization performance of the IHHO is meaningful.

Fast search block matching algorithm (BMA)-based video coding provides reasonable good quality video with minute cost of computation. In fast BMA, clock cycles required to read pixel data are quite more compared with matching operation due to erratic location of candidate macroblocks (CMBs). With aim of reduction in number of clock cycles, parallel memory system is used in this study, which can accelerate reading of CMBs and speedup motion vector (MV) computation. Novel concept of register array is introduced to organise CMBs, which expedite computation hungry search process. Owing to shape of register array, lesser space is needed to store CMBs and architecture addresses wide range of search patterns. The proposed sum of absolute difference processor with parallel memory system computes MV of 1 macroblock in 28 clock cycles in average case. Compared to single memory system, it saves 68% and 80% clock cycles in CMB access of initial search and intermediate search process, respectively. Hardware architecture is tested with Xilinx Virtex5 field programmable gate array. The proposed fixed 8×8 macroblock size architecture processes 354 high definition (HD) (1080p) frames per second (fps) and configurable architecture processes 201 HD fps which is more than adequate for real-time encoding.

In a real Hilbert space, we denote CFPP and VIP as common fixed point problem of finitely many strict pseudocontractions and a variational inequality problem for Lipschitzian, pseudomonotone operator, respectively. This paper is devoted to explore how to find a common solution of the CFPP and VIP. To this end, we propose Mann viscosity algorithms with line-search process by virtue of subgradient extragradient techniques. The designed algorithms fully assimilate Mann approximation approach, viscosity iteration algorithm and inertial subgradient extragradient technique with line-search process. Under suitable assumptions, it is proven that the sequences generated by the designed algorithms converge strongly to a common solution of the CFPP and VIP, which is the unique solution to a hierarchical variational inequality (HVI).

In a real Hilbert space, let the CFPP, VIP, and HFPP denote the common fixed-point problem of countable nonexpansive operators and asymptotically nonexpansive operator, variational inequality problem, and hierarchical fixed point problem, respectively. With the help of the Mann iteration method, a subgradient extragradient approach with a linear-search process, and a hybrid deepest-descent technique, we construct two modified Mann-type subgradient extragradient rules with a linear-search process for finding a common solution of the CFPP and VIP. Under suitable assumptions, we demonstrate the strong convergence of the suggested rules to a common solution of the CFPP and VIP, which is only a solution of a certain HFPP.

Let VIP indicate the variational inequality problem with Lipschitzian and pseudomonotone operator and let CFPP denote the common fixed-point problem of an asymptotically nonexpansive mapping and a strictly pseudocontractive mapping in a real Hilbert space. Our object in this article is to establish strong convergence results for solving the VIP and CFPP by utilizing an inertial-like gradient-like extragradient method with line-search process. Via suitable assumptions, it is shown that the sequences generated by such a method converge strongly to a common solution of the VIP and CFPP, which also solves a hierarchical variational inequality (HVI).

The study investigated the level of information literacy skills and use of EIRs through the application of Kuhlthau's Information Search Process (ISP) by undergraduate students of Federal Universities in Nigeria and developed a conceptual model framework to represent the findings. The study adopted the principles of positivist assumptions using Cross-Sectional survey and a Two-Stage Cluster sampling technique on a sampled 2,402 undergraduate students. Data were collected using questionnaire and analyzed through descriptive statistics. The study found out that the Model is effective to this study as it describes the information search process from the perspective of the user which developed a principle of uncertainty that initiates the process of information seeking. The findings revealed that undergraduate students have adequate knowledge about information literacy program (mean=4.0) and are aware of a programs that teach users about how to use e-resources in seeking for information to increase self awareness (mean=4.3). The study found out that ICT facilities enable undergraduate students acquire more information (90.3%) and have adequate knowledge about information literacy program and search terminologies (1,034/51.2%) which initiate the search process. The general conclusion of the research is that undergraduate students have adequate information literacy skills but experience heightened uncertainty with inconsistent information that requires construction and interpretation. The study recommended that librarians need to re-evaluate their roles and be more active in the task of inculcating the principles of information literacy and apply its various aspects in appropriate situations for the promotion of information literacy programs.

We propose two Mann-type subgradient-like extra gradient iterations with the line-search procedure for hierarchical variational inequality (HVI) with the common fixed-point problem (CFPP) constraint of finite family of nonexpansive mappings and an asymptotically nonexpansive mapping in a real Hilbert space. Our methods include combinations of the Mann iteration method, subgradient extra gradient method with the line-search process, and viscosity approximation method. Under suitable assumptions, we obtain the strong convergence results of sequence of iterates generated by our methods for a solution to HVI with the CFPP constraint.

In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search and rescue operations in disaster zones, and to genetic computer algorithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent α ≤ 1 are generally believed to optimize the search process. However, most search spaces present complex topographies. What is the best search strategy in these more realistic scenarios? Here, we show that the topography of the environment significantly alters the optimal search strategy toward less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for nonregenerating sparse targets in a 2D space with steps drawn from a Lévy distribution with the exponent varying from α = 1 to α = 2 (Brownian). We show that, when boundaries, barriers, and obstacles are present, the optimal search strategy depends on the topography of the environment, with α assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher’s size. Our results are relevant for search problems at different length scales from animal and human foraging to microswimmers’ taxis to biochemical rates of reaction.

Deaf children traditionally face greater challenges and encounter difficulties particular to their abilities. The Information Search Process model (ISP) is oriented in the context of the present work as a process of searching for information about children with a hearing impairment in the area of literacy, applying the different stages of the ISP model to identify different aspects of hearing impairment. A case study was carried out at the USAER school in Aguascalientes, Mexico, a school that enables children with hearing impairments to be included in regular secondary schooling. The children here are aged 12–15 years and have literacy problems. Eight children from 7 to 11 years of age were also evaluated in the Institute for Deaf and Blind Children in Cali, Colombia. The ISP model can be deemed to have been useful for identifying the relevant needs to support both teachers and deaf children. A need was identified, to construct a game that integrates the activities undertaken following the FitzGerald keys and in turn allow them to assess learning through different activities that help increase the semantic repertoire.

This paper introduces a binary search algorithm for determining the optimal probability cut-point value (C) of binary classifiers. Cut-points are operating points on the receiver operating characteristic curve that divide positive and negative predictions. Compared to the traditional exhaustive search for optimal C value, the proposed method offers execution time efficiency (O(log2(k))) and a small cut-point error of 1/2n after k steps of binary search. Traditionally, the optimal C value is determined by stepping through all possible C values. This search is uninformed because there is no indication of the search direction. To address this issue, we derive the expectation of the F-Measure (aka F1 score); and use it to guide the search process. Specifically, by comparing the F-Measure at the current cut-point with the F-Measure at expected cut-point, we can use the information to adjust C dynamically towards the optimal cut-point, resulting in optimal model performance. Our results on two classifiers trained from disease classification datasets suggest that the algorithm is robust and efficient, as compared to the traditional methods.