Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Development of Cationic Solid Lipid Nanoparticles with Factorial Design-Based Studies for Topical Administration of Doxorubicin

Topical chemotherapy using doxorubicin, a powerful anticancer drug, can be used as an alternative with reduced systemic toxicity when treating skin cancer. The aim of the present work was to use factorial design-based studies to develop cationic solid lipid nanoparticles containing doxorubicin; further investigations into the influence of these particles on the drug's cytotoxicity and cellular uptake in B16F10 murine melanoma cells were performed. A 3(2) full factorial design was applied for two different lipid phases; one phase used stearic acid and the other used a 1:2 mixture of stearic acid and glyceryl behenate. The two factors investigated included the ratio between the lipid and the water phase and the ratio between the surfactant (poloxamer) and the co-surfactant (cetylpyridinium chloride). It was observed that the studied factors did not affect the mean diameter or the polydispersity of the obtained nanoparticles; however, they did significantly affect the zeta potential values. Optimised formulations with particle sizes ranging from 251 to 306 nm and positive zeta potentials were selected for doxorubicin incorporation. High entrapment efficiencies were achieved (97%) in formulations with higher amounts of stearic acid, suggesting that cationic charges on doxorubicin molecules may interact with the negative charges in stearic acid. Melanoma culture cell experiments showed that cationic solid lipid nanoparticles without drug were not cytotoxic to melanoma cells. The encapsulation of doxorubicin significantly increased cytotoxicity, indicating the potential of these nanoparticles for the treatment of skin cancer.

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

Blood-brain barrier (BBB) permeation is an essential property for drugs that act in the central nervous system (CNS) for the treatment of human diseases, such as epilepsy, depression, Alzheimer's disease, Parkinson disease, schizophrenia, among others. In the present work, quantitative structure-property relationship (QSPR) studies were conducted for the development and validation of in silico models for the prediction of BBB permeation. The data set used has substantial chemical diversity and a relatively wide distribution of property values. The generated QSPR models showed good statistical parameters and were successfully employed for the prediction of a test set containing 48 compounds. The predictive models presented herein are useful in the identification, selection and design of new drug candidates having improved pharmacokinetic properties.

Reliability-based design optimization strategies based on FORM: a review

In deterministic optimization, the uncertainties of the structural system (i.e. dimension, model, material, loads, etc) are not explicitly taken into account. Hence, resulting optimal solutions may lead to reduced reliability levels. The objective of reliability based design optimization (RBDO) is to optimize structures guaranteeing that a minimum level of reliability, chosen a priori by the designer, is maintained. Since reliability analysis using the First Order Reliability Method (FORM) is an optimization procedure itself, RBDO (in its classical version) is a double-loop strategy: the reliability analysis (inner loop) and the structural optimization (outer loop). The coupling of these two loops leads to very high computational costs. To reduce the computational burden of RBDO based on FORM, several authors propose decoupling the structural optimization and the reliability analysis. These procedures may be divided in two groups: (i) serial single loop methods and (ii) unilevel methods. The basic idea of serial single loop methods is to decouple the two loops and solve them sequentially, until some convergence criterion is achieved. On the other hand, uni-level methods employ different strategies to obtain a single loop of optimization to solve the RBDO problem. This paper presents a review of such RBDO strategies. A comparison of the performance (computational cost) of the main strategies is presented for several variants of two benchmark problems from the literature and for a structure modeled using the finite element method.