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

An Analysis of Solution Properties of the Graph Coloring Problem

This paper concerns the analysis of solution properties of the Graph Coloring Problem. For this purpose, we introduce a property based on the notion of representative sets which are sets of vertices that are always colored the same in a set of solutions. Experimental results on well-studied DIMACS graphs show that many of them contain such sets and give interesting information about the diversity of the solutions. We also show how such an analysis may be used to improve a tabu search algorithm

Development of new malaria diagnostics: matching performance and need

Despite advances in diagnostic technology, significant gaps remain in access to malaria diagnosis. Accurate diagnosis and misdiagnosis leads to unnecessary waste of resources, poor disease management, and contributes to a cycle of poverty in low-resourced communities. Despite much effort and investment, few new technologies have reached the field in the last 30 years aside from lateral flow assays. This suggests that much diagnostic development effort has been misdirected, and/or that there are fundamental blocks to introduction of new technologies. Malaria diagnosis is a difficult market; resources are broadly donor-dependent, health systems in endemic countries are frequently weak, and the epidemiology of malaria and priorities of malaria programmes and donors are evolving. Success in diagnostic development will require a good understanding of programme gaps, and the sustainability of markets to address them. Targeting assay development to such clearly defined market requirements will improve the outcomes of product development funding. Six market segments are identified: (1) case management in low-resourced countries, (2) parasite screening for low density infections in elimination programmes, (3) surveillance for evidence of continued transmission, (4) clinical research and therapeutic efficacy monitoring, (5) cross-checking for microscopy quality control, and (6) returned traveller markets distinguished primarily by resource availability. While each of these markets is potentially compelling from a public health standpoint, size and scale are highly variable and continue to evolve. Consequently, return on investment in research and development may be limited, highlighting the need for potentially significant donor involvement or the introduction of novel business models to overcome prohibitive economics. Given the rather specific applications, a well-defined set of stakeholders will need to be on board for the successful introduction and scaling of any new technology to these markets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12936-016-1454-8) contains supplementary material, which is available to authorized users

QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization

Background: The visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain “hidden regularities” and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects. Methodology/Principal Findings: We present a new data visualization approach (QAPgrid) that reveals patterns of similarities and differences in large datasets of objects for which a similarity measure can be computed. Objects are assigned to positions on an underlying square grid in a two-dimensional space. We use the Quadratic Assignment Problem (QAP) as a mathematical model to provide an objective function for assignment of objects to positions on the grid. We employ a Memetic Algorithm (a powerful metaheuristic) to tackle the large instances of this NP-hard combinatorial optimization problem, and we show its performance on the visualization of real data sets. Conclusions/Significance: Overall, the results show that QAPgrid algorithm is able to produce a layout that represents the relationships between objects in the data set. Furthermore, it also represents the relationships between clusters that are feed into the algorithm. We apply the QAPgrid on the 84 Indo-European languages instance, producing a near-optimal layout. Next, we produce a layout of 470 world universities with an observed high degree of correlation with the score used by the Academic Ranking of World Universities compiled in the The Shanghai Jiao Tong University Academic Ranking of World Universities without the need of an ad hoc weighting of attributes. Finally, our Gene Ontology-based study on Saccharomyces cerevisiae fully demonstrates the scalability and precision of our method as a novel alternative tool for functional genomics

Timetable-based operation in urban transport: Run-time optimisation and improvements in the operating process

Urban public transit provides an efficient means of mobility and helps support social development and environmental preservation. To avoid loss of ridership, transit authorities have focussed on improving the punctuality of routes that operate using timetables. This paper presents a new approach to generating run-time values that is based on analytical development and micro simulations. The work utilizes previous research (described herein) and the experience acquired by Transports Metropolitans de Barcelona (TMB) in operating bus routes based on timetables. Using a sample of historical data, the method used for generating run-time values consists of the following steps: purging and screening atypical trips, based on the consideration of confidence intervals for median trips; segmenting the day into time bands based on the introduction of a new hierarchical classification algorithm; creating initial run-time values based on criteria derived from statistical analysis; adjusting and validating initial run-time values using micro simulations; and evaluating incident-recovery times at the end of trips in order to guarantee the punctual departure of the next trip in the vehicle schedule. To favour service improvement, we also introduced certain indicators that can identify the root causes of non-compliance. As a final step, in order to ensure the applicability and use of the model, we promoted the development of our model within the framework of the HASTUS(TM) software solution.Timetables Run times Punctuality Bus transportation Software development Control and improvement

A Self-Adaptive Heuristic Algorithm for Combinatorial Optimization Problems

This paper introduces a new self-tuning mechanism to the local search heuristic for solving of combinatorial optimization problems. Parameter tuning of heuristics makes them difficult to apply, as parameter tuning itself is an optimization problem. For this purpose, a modified local search algorithm free from parameter tuning, called Self-Adaptive Local Search (SALS), is proposed for obtaining qualified solutions to combinatorial problems within reasonable amount of computer times. SALS is applied to several combinatorial optimization problems, namely, classical vehicle routing, permutation flow-shop scheduling, quadratic assignment, and topological design of networks. It is observed that self-adaptive structure of SALS provides implementation simplicity and flexibility to the considered combinatorial optimization problems. Detailed computational studies confirm the performance of SALS on the suit of test problems for each considered problem type especially in terms of solution quality