Michigan State University

About: Michigan State University is a education organization based out in East Lansing, Michigan, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 60109 authors who have published 137074 publications receiving 5633022 citations. The organization is also known as: MSU & Michigan State.

An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach

Abstract: In the precursor paper, a many-objective optimization method (NSGA-III), based on the NSGA-II framework, was suggested and applied to a number of unconstrained test and practical problems with box constraints alone. In this paper, we extend NSGA-III to solve generic constrained many-objective optimization problems. In the process, we also suggest three types of constrained test problems that are scalable to any number of objectives and provide different types of challenges to a many-objective optimizer. A previously suggested MOEA/D algorithm is also extended to solve constrained problems. Results using constrained NSGA-III and constrained MOEA/D show an edge of the former, particularly in solving problems with a large number of objectives. Furthermore, the NSGA-III algorithm is made adaptive in updating and including new reference points on the fly. The resulting adaptive NSGA-III is shown to provide a denser representation of the Pareto-optimal front, compared to the original NSGA-III with an identical computational effort. This, and the original NSGA-III paper, together suggest and amply test a viable evolutionary many-objective optimization algorithm for handling constrained and unconstrained problems. These studies should encourage researchers to use and pay further attention in evolutionary many-objective optimization.

Generality of leaf trait relationships: a test across six biomes

Abstract: Convergence in interspecific leaf trait relationships across diverse taxonomic groups and biomes would have important evolutionary and ecological implications. Such convergence has been hypothesized to result from trade-offs that limit the combination of plant traits for any species. Here we address this issue by testing for biome differences in the slope and intercept of interspecific relationships among leaf traits: longevity, net pho- tosynthetic capacity (Amax), leaf diffusive conductance (Gs), specific leaf area (SLA), and nitrogen (N) status, for more than 100 species in six distinct biomes of the Americas. The six biomes were: alpine tundra-subalpine forest ecotone, cold temperate forest-prairie ecotone, montane cool temperate forest, desert shrubland, subtropical forest, and tropical rain forest. Despite large differences in climate and evolutionary history, in all biomes mass-based leaf N (Nmass), SLA, Gs, and Amax were positively related to one another and decreased with increasing leaf life span. The relationships between pairs of leaf traits exhibited similar slopes among biomes, suggesting a predictable set of scaling relationships among key leaf morphological, chemical, and metabolic traits that are replicated globally among terrestrial ecosystems regardless of biome or vegetation type. However, the intercept (i.e., the overall elevation of regression lines) of relationships between pairs of leaf traits usually differed among biomes. With increasing aridity across sites, species had greater Amax for a given level of Gs and lower SLA for any given leaf life span. Using principal components analysis, most variation among species was explained by an axis related to mass-based leaf traits (Amax, N, and SLA) while a second axis reflected climate, Gs, and other area-based leaf traits.

The ReaxFF reactive force-field: development, applications and future directions

Abstract: The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method.