A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring

The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the intelligent behavior of a honey bee swarm. It is widely recognized as a quick, reliable, and efficient methods for solving optimization problems. This paper proposes a hybrid ABC (HABC) algorithm for graph 3-coloring, which is a well-known discrete optimization problem. The results of HABC are compared with results of the well-known graph coloring algorithms of today, i.e. the Tabucol and Hybrid Evolutionary algorithm (HEA) and results of the traditional evolutionary algorithm with SAW method (EA-SAW). Extensive experimentations has shown that the HABC matched the competitive results of the best graph coloring algorithms, and did better than the traditional heuristics EA-SAW when solving equi-partite, flat, and random generated medium-sized graphs

Optimality Clue for Graph Coloring Problem

In this paper, we present a new approach which qualifies or not a solution found by a heuristic as a potential optimal solution. Our approach is based on the following observation: for a minimization problem, the number of admissible solutions decreases with the value of the objective function. For the Graph Coloring Problem (GCP), we confirm this observation and present a new way to prove optimality. This proof is based on the counting of the number of different k-colorings and the number of independent sets of a given graph G. Exact solutions counting problems are difficult problems (\#P-complete). However, we show that, using only randomized heuristics, it is possible to define an estimation of the upper bound of the number of k-colorings. This estimate has been calibrated on a large benchmark of graph instances for which the exact number of optimal k-colorings is known. Our approach, called optimality clue, build a sample of k-colorings of a given graph by running many times one randomized heuristic on the same graph instance. We use the evolutionary algorithm HEAD [Moalic et Gondran, 2018], which is one of the most efficient heuristic for GCP. Optimality clue matches with the standard definition of optimality on a wide number of instances of DIMACS and RBCII benchmarks where the optimality is known. Then, we show the clue of optimality for another set of graph instances. Optimality Metaheuristics Near-optimal

A Recombination-Based Tabu Search Algorithm for the Winner Determination Problem

Abstract. We propose a dedicated tabu search algorithm (TSX_WDP) for the winner determination problem (WDP) in combinatorial auctions. TSX_WDP integrates two complementary neighborhoods designed re-spectively for intensification and diversification. To escape deep local optima, TSX_WDP employs a backbone-based recombination opera-tor to generate new starting points for tabu search and to displace the search into unexplored promising regions. The recombination operator operates on elite solutions previously found which are recorded in an global archive. The performance of our algorithm is assessed on a set of 500 well-known WDP benchmark instances. Comparisons with five state of the art algorithms demonstrate the effectiveness of our approach

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Diabetic β-Cells Can Achieve Self-Protection against Oxidative Stress through an Adaptive Up-Regulation of Their Antioxidant Defenses

Background Oxidative stress (OS), through excessive and/or chronic reactive oxygen species (ROS), is a mediator of diabetes-related damages in various tissues including pancreatic β-cells. Here, we have evaluated islet OS status and β-cell response to ROS using the GK/Par rat as a model of type 2 diabetes. Methodology/Principal Findings Localization of OS markers was performed on whole pancreases. Using islets isolated from 7-day-old or 2.5-month-old male GK/Par and Wistar control rats, 1) gene expression was analyzed by qRT-PCR; 2) insulin secretion rate was measured; 3) ROS accumulation and mitochondrial polarization were assessed by fluorescence methods; 4) antioxidant contents were quantified by HPLC. After diabetes onset, OS markers targeted mostly peri-islet vascular and inflammatory areas, and not islet cells. GK/Par islets revealed in fact protected against OS, because they maintained basal ROS accumulation similar or even lower than Wistar islets. Remarkably, GK/Par insulin secretion also exhibited strong resistance to the toxic effect of exogenous H2O2 or endogenous ROS exposure. Such adaptation was associated to both high glutathione content and overexpression (mRNA and/or protein levels) of a large set of genes encoding antioxidant proteins as well as UCP2. Finally, we showed that such a phenotype was not innate but spontaneously acquired after diabetes onset, as the result of an adaptive response to the diabetic environment. Conclusions The GK/Par model illustrates the effectiveness of adaptive response to OS by beta-cells to achieve self-tolerance. It remains to be determined to what extend such islet antioxidant defenses upregulation might contribute to GK/Par beta-cell secretory dysfunction

On a Clique-Based Integer Programming Formulation of Vertex Colouring with Applications in Course Timetabling

Vertex colouring is a well-known problem in combinatorial optimisation, whose alternative integer programming formulations have recently attracted considerable attention. This paper briefly surveys seven known formulations of vertex colouring and introduces a formulation of vertex colouring using a suitable clique partition of the graph. This formulation is applicable in timetabling applications, where such a clique partition of the conflict graph is given implicitly. In contrast with some alternatives, the presented formulation can also be easily extended to accommodate complex performance indicators (``soft constraints'') imposed in a number of real-life course timetabling applications. Its performance depends on the quality of the clique partition, but encouraging empirical results for the Udine Course Timetabling problem are reported

Deletion of methylglyoxal synthase gene (mgsA) increased sugar co-metabolism in ethanol-producing Escherichia coli

The use of lignocellulose as a source of sugars for bioproducts requires the development of biocatalysts that maximize product yields by fermenting mixtures of hexose and pentose sugars to completion. In this study, we implicate mgsA encoding methylglyoxal synthase (and methylglyoxal) in the modulation of sugar metabolism. Deletion of this gene (strain LY168) resulted in the co-metabolism of glucose and xylose, and accelerated the metabolism of a 5-sugar mixture (mannose, glucose, arabinose, xylose and galactose) to ethanol