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To improve the shortcomings of the sparrow search algorithm (SSA), such as the loss of diversity due to inadequate population initialization results and the susceptibility to interference from individual location information during exploration and exploitation, which affect the accuracy of the search, an improved sparrow search algorithm based on fusion ranking elastic collision, referred to as XSSA, is proposed. Firstly, an improved iterative chaotic map with infinite collapses (ICMIC) improves the dispersion degree of the initial population distribution. Then, the Gaussian random walk strategy is used to balance the exploration and development capabilities of the algorithm. In addition, sorted elastic collision policy is imposed on all individuals after the discoverer is updated, which avoids premature convergence of the algorithm to the local extreme value. Finally, a multi-strategy boundary processing mechanism is formulated according to the characteristics of optimization at different stages to retain the population and avoid the loss of diversity. At the same time, the position of individuals beyond the boundary is re-updated in combination with important position information, so that the processed position is more reasonable and provides quality guarantee for the next iterative search. The simulation experiments are performed on 12 benchmark functions, and the convergence accuracy graph demonstrates the performance of the algorithm. By means of contribution tests for each strategy, Wilcoxon rank sum test and the comprehensive ranking of Friedman test, the effectiveness, uniqueness and better optimization performance of XSSA are proven.

Abstract This study proposes that the foaming pre-dispersion technology is combined with the gas-phase-assisted spray technology, and a foaming agent potassium oleate is introduced. The volume expansion power generated by [...]

High amylase level is a typical sign of acute pancreatitis. However， it can also be found in other diseases. In the article， a case of persistent elevation of amylase level for over 1 year was reported. A 66-year-old male patient suffered from bullous pemphigoid complicated with diabetes mellitus for 3 years. After bullous pemphigoid treatment 8 months ago， he presented with elevated amylase level ranging from 556 to 1106 U/L， serum lipase level between 62 U/L and 73 U/L and urine amylase level between 553 U/L and 1162 U/L， respectively. The renal amylase clearance rate /creatinine clearance ratio was 0.8% （

To improve the performance of the sparrow search algorithm in solving complex optimization problems, this study proposes a novel variant called the Improved Beetle Antennae Search-Based Sparrow Search Algorithm (IBSSA). A new elite dynamic opposite learning strategy is proposed in the population initialization stage to enhance population diversity. In the update stage of the discoverer, a staged inertia weight guidance mechanism is used to improve the update formula of the discoverer, promote the information exchange between individuals, and improve the algorithm's ability to optimize on a global level. After the follower's position is updated, the logarithmic spiral opposition-based learning strategy is introduced to disturb the initial position of the individual in the beetle antennae search algorithm to obtain a more purposeful solution. To address the issue of decreased diversity and susceptibility to local optima in the sparrow population during later stages, the improved beetle antennae search algorithm and sparrow search algorithm are combined using a greedy strategy. This integration aims to improve convergence accuracy. On 20 benchmark test functions and the CEC2017 Test suite, IBSSA performed better than other advanced algorithms. Moreover, six engineering optimization problems were used to demonstrate the improved algorithm's effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have garnered much attention among researchers owing to their robustness, adaptability, independence from a predetermined initial solution, and lack of reliance on gradient computations. The flower pollination algorithm (FPA) and the slime mould algorithm (SMA) are efficient methodologies for addressing global optimization challenges. Nonetheless, tackling large-scale global problems using a single algorithm often proves challenging due to inherent limitations in its mechanism. One effective approach to mitigating this limitation is to hybrid the two algorithms employing suitable strategies. We proposed a hybrid algorithm (GFPSMA) based on FPA and SMA. First, to address the global exploration issue of FPA, a method was proposed that utilized the golden section mechanism to enhance information exchange between random individuals and the best individual. Second, to improve the reliability of the random search phase in SMA, an adaptive step-size strategy was introduced. Furthermore, a dual-competition mechanism, inspired by gaming concepts, was introduced to enhance the integration of the two algorithms. Finally, an elite learning method with adjustment conditions was employed to refine the localization of the best individual. To assess the performance advantage of GFPSMA, 39 benchmark functions were employed, comparing GFPSMA with FPA and SMA along with their six variants, six variants of other metaheuristic algorithms, three CEC competition algorithms, totaling 17 algorithms, and strategic algorithms for testing. Experimental results demonstrated the favorable performance advantage of GFPSMA. Additionally, the feasibility and practicality of GFPSMA were demonstrated in four engineering problems. [ABSTRACT FROM AUTHOR]