An Iron Refractory Phenotype in Obese Adipose Tissue Macrophages Leads to Adipocyte Iron Overload

Adipocyte iron overload is a maladaptation associated with obesity and insulin resistance. The objective of the current study was to determine whether and how adipose tissue macrophages (ATMs) regulate adipocyte iron concentrations and whether this is impacted by obesity. Using bone marrow-derived macrophages (BMDMs) polarized to M0, M1, M2, or metabolically activated (MMe) phenotypes, we showed that MMe BMDMs and ATMs from obese mice have reduced expression of several iron-related proteins. Furthermore, the bioenergetic response to iron in obese ATMs was hampered. ATMs from iron-injected lean mice increased their glycolytic and respiratory capacities, thus maintaining metabolic flexibility, while ATMs from obese mice did not. Using an isotope-based system, we found that iron exchange between BMDMs and adipocytes was regulated by macrophage phenotype. At the end of the co-culture, MMe macrophages transferred and received more iron from adipocytes than M0, M1, and M2 macrophages. This culminated in a decrease in total iron in MMe macrophages and an increase in total iron in adipocytes compared with M2 macrophages. Taken together, in the MMe condition, the redistribution of iron is biased toward macrophage iron deficiency and simultaneous adipocyte iron overload. These data suggest that obesity changes the communication of iron between adipocytes and macrophages and that rectifying this iron communication channel may be a novel therapeutic target to alleviate insulin resistance.     

A non-linear optimal control approach to central sewer network flow control

The problem of optimal water distribution to several retention reservoirs in an urban sewer network during rainfall is considered. The goal of the control actions is the minimization of overflows and eventually the reduction of their polluting impact on receiving waters. To this end, a non-linear optimal control approach is used and the numerical solution of the control problem is effectuated by use of a feasible direction algorithm. A detailed study of the central control problem for a particular large sewer network using this method is presented. Results demonstrate the efficiency and the real-time feasibility of the developed methodology.     

Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem

This paper introduces a new algorithmic nature-inspired approach that uses particle swarm optimization (PSO) with different neighborhood topologies, for successfully solving one of the most computationally complex problems, the permutation flowshop scheduling problem (PFSP). The PFSP belongs to the class of combinatorial optimization problems characterized as NP-hard and, thus, heuristic and metaheuristic techniques have been used in order to find high quality solutions in reasonable computational time. The proposed algorithm for the solution of the PFSP, the PSO with expanding neighborhood topology, combines a PSO algorithm, the variable neighborhood search strategy and a path relinking strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) in each iteration the size of the neighborhood, it ends to a neighborhood that includes all the swarm, and it manages to take advantage of the exploration abilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method, we use a set of benchmark instances of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.     

Ant colony and particle swarm optimization for financial classification problems

Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. Such models ought to be as accurate as possible. One important step towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. This is known as the feature selection problem in the machine learning/data mining field. In financial decisions, feature selection is often based on the subjective judgment of the experts. Nevertheless, automated feature selection algorithms could be of great help to the decision-makers providing the means to explore efficiently the solution space. This study uses two nature-inspired methods, namely ant colony optimization and particle swarm optimization, for this problem. The modelling context is developed and the performance of the methods is tested in two financial classification tasks, involving credit risk assessment and audit qualifications.     

A Hybrid Multi-Swarm Particle Swarm Optimization algorithm for the Probabilistic Traveling Salesman Problem

The Probabilistic Traveling Salesman Problem (PTSP) is a variation of the classic Traveling Salesman Problem (TSP) and one of the most significant stochastic routing problems. In the PTSP, only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a new hybrid algorithmic nature inspired approach based on Particle Swarm Optimization (PSO), Greedy Randomized Adaptive Search Procedure (GRASP) and Expanding Neighborhood Search (ENS) Strategy is proposed for the solution of the PTSP. The proposed algorithm is tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm, the classic PSO and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in 13 out of 20 cases the proposed algorithm gives a new best solution.     

Fuzzy control optimized by a Multi-Objective Particle Swarm Optimization algorithm for vibration suppression of smart structures

Smart structures include elements of active, passive or hybrid control. In this paper, a new Multi-Objective Particle Swarm Optimization (MOPSO), with a different velocity equation, for the calculation of the free parameters in active control systems is proposed and tested. A fuzzy control system is considered. Fuzzy control is a suitable tool for the systematic development of nonlinear active control strategies and can be fine tuned if no experience exists or if one designs more complicated control schemes. The usage of MOPSO with a combination of continuous and discrete variables for the optimal design of the controller is proposed. Numerical applications on smart piezoelastic beams are presented.     

Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization

This paper presents the design of a vibration control mechanism for a beam with bonded piezoelectric sensors and actuators. The mechanical modeling of the structure and the subsequent finite element approximation are based on the classical equations of motion, as they are derived from Hamilton’s principle, in connection with simplified modeling of the piezoelectric sensors and actuators. One nature-inspired intelligence method, the Particle Swarm Optimization, is used for the vibration control of the beam. Three different variants of the Particle Swarm Optimization were tested, namely, the simple Particle Swarm Optimization, the inertia Particle Swarm Optimization and the Constriction Particle Swarm Optimization. A linear feedback control law and a quadratic cost function are used, so that the results are comparable with the classical linear quadratic regulator approach. The same problem has been solved with two other stochastic based optimization algorithms, namely a Genetic Algorithm and a Differential Evolution and the results are used for comparison. The numerical simulation shows that sufficient vibration suppression can be achieved by means of this method.     

Honey Bees Mating Optimization algorithm for financial classification problems

Nature inspired methods are approaches that are used in various fields and for the solution for a number of problems. This study uses a nature inspired method, namely Honey Bees Mating Optimization, that is based on the mating behaviour of honey bees for a financial classification problem. Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. One important step towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. The proposed method uses for the feature selection step, the Honey Bees Mating Optimization algorithm while for the classification step, Nearest Neighbor based classifiers are used. The performance of the method is tested in a financial classification task involving credit risk assessment. The results of the proposed method are compared with the results of a particle swarm optimization algorithm, an ant colony optimization, a genetic algorithm and a tabu search algorithm.