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The consumption of non-nutritive sweeteners has increased in the last decades. However, there are doubts about its consumption and its impact on body mass and metabolic alterations. For this reason, this study investigates the effects of the consumption of nutritive and non-nutritive sweeteners on body mass in different life stages of male and female Wistar rats: Childhood, adolescence, young adult, adulthood, and aged. For this purpose, 8 groups of male and female rats were used (10 per group/gender): sucrose 10%, glucose 14%, fructose 7%, acesulfame K 0.05%, aspartame:acesulfame mixture 1.55%, sucralose 0.017%, saccharin 0.033%, and a control group. Only in aged male rats (504 days) there were significant differences in body mass. In both genders, there were differences in food, drink, and energy intake along all life stage. It is concluded that non-nutritive sweeteners when consumed together with a balanced diet did not cause a greater body mass gain. [ABSTRACT FROM AUTHOR]

Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao's algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed. [ABSTRACT FROM AUTHOR]