Jiangsu Normal University

About: Jiangsu Normal University is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Laser & Fiber laser. The organization has 5773 authors who have published 6538 publications receiving 96198 citations. The organization is also known as: Xuzhou Normal University & JSNU.

Monarch butterfly optimization

Abstract: In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub ( https://github.com/ggw0122/Monarch-Butterfly-Optimization , C++/MATLAB) and MATLAB Central ( http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization , MATLAB).

Brønsted-Acid-Catalyzed Asymmetric Multicomponent Reactions for the Facile Synthesis of Highly Enantioenriched Structurally Diverse Nitrogenous Heterocycles

Abstract: Optically pure nitrogenous compounds, and especially nitrogen-containing heterocycles, have drawn intense research attention because of their frequent isolation as natural products. These compounds have wide-ranging biological and pharmaceutical activities, offering potential as new drug candidates. Among the various synthetic approaches to nitrogenous heterocycles, the use of asymmetric multicomponent reactions (MCRs) catalyzed by chiral phosphoric acids has recently emerged as a particularly robust tool. This method combines the prominent merits of MCRs with organocatalysis, thus affording enantio-enriched nitrogenous heterocyclic compounds with excellent enantioselectivity, atom economy, bond-forming efficiency, structural diversity, and complexity. In this Account, we discuss a variety of asymmetric MCRs catalyzed by chiral phosphoric acids that lead to the production of structurally diverse nitrogenous heterocycles.In MCRs, three or more reagents are combined simultaneously to produce a single produc...

Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems

Abstract: Phototaxis, signifying movement of an organism towards or away from a source of light, is one of the most representative features for moths. It has recently been shown that one of the characteristics of moths has been the propensity to follow Levy flights. Inspired by the phototaxis and Levy flights of the moths, a new kind of metaheuristic algorithm, called moth search (MS) algorithm, is developed in the present work. In nature, moths are a family insects associated with butterflies belonging to the order Lepidoptera. In MS method, the best moth individual is viewed as the light source. Some moths that are close to the fittest one always display an inclination to fly around their own positions in the form of Levy flights. On the contrary, due to phototaxis, the moths that are comparatively far from the fittest one will tend to fly towards the best one directly in a big step. These two features correspond to the processes of exploitation and exploration of any metaheuristic optimization method. The phototaxis and Levy flights of the moths can be used to build up a general-purpose optimization method. In order to demonstrate the superiority of its performance, the MS method is further compared with five other state-of-the-art metaheuristic optimization algorithms through an array of experiments on fourteen basic benchmarks, eleven IEEE CEC 2005 complicated benchmarks and seven IEEE CEC 2011 real world problems. The results clearly demonstrate that MS significantly outperforms five other methods on most test functions and engineering cases.