A series of α-diimine ligands with different substituents on the acenaphthyl backbone were synthesized and characterized. The corresponding Ni(II) and Pd(II) complexes were prepared and used in ethylene polymerization and copolymerization with methyl acrylate. In ethylene polymerization, these Ni(II) complexes showed activities of up to 1.6 × 10 7 g/((mol of Ni) h), generating polyethylene with a molecular weight (M n ) of up to 4.2 × 10 5 . Interestingly, these Ni(II) complexes behave very similarly in ethylene polymerization except for the complex with two methoxy substituents on the ortho position of the acenaphthyl backbone, in which case about 3 times higher polyethylene molecular weight and much lower branching density were observed. The ligand substituent effect is much more dramatic for the Pd(II) complexes. In ethylene polymerization, activities of up to 1.7 × 10 5 g/((mol of Pd) h) and a polyethylene molecular weight (M n ) of up to 4.7 × 10 4 could be obtained. The Pd(II) complex with two methoxy substituents on the ortho position of the acenaphthyl backbone demonstrated much higher activity and generated polyethylene with about 3 times higher molecular weight than that for the classic Pd(II) complex. A similar trend was maintained in ethylene−methyl acrylate copolymerization.
Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.