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The static scheduling of independent tasks on homogeneous multiprocessor systems is studied in this paper. This problem is treated by the Bee Colony Optimization (BCO) meta-heuristic. The BCO algorithm belongs to the class of stochastic swarm optimization methods inspired by the foraging habits of bees in nature. To investigate the performance of the proposed method extensive numerical experiments are performed. Our BCO algorithm is able to obtain the optimal value of the objective function in the majority of test examples known from literature. The deviation of non-optimal solutions from the optimal ones in our test examples is at most 2%. The CPU times required to find the best solutions by BCO are significantly smaller than the corresponding times required by the CPLEX optimization solver. Moreover, our BCO is competitive with state-of-the-art methods for similar problems, with respect to both solution quality and running time. The stability of BCO is examined through multiple executions and it is shown that solution deviation is less than 1%.T. Davidović ( ) Mathematical Institute, Serbian Academy of Sciences and Arts,

Abstract-Early classification of time series has been receiving a lot of attention as of late, particularly in the context of gene expression. In the biomedical realm, early classification can be of tremendous help, by identifying the onset of a disease before it has time to fully take hold, or determining that a treatment has done its job and can be discontinued. In this paper we present a state-of-the-art model, which we call the Early Classification Model (ECM), that allows for early, accurate, and patient-specific classification of multivariate time series. The model is comprised of an integration of the widelyused HMM and SVM models, which, while not a new technique per se, has not been used for early classification of multivariate time series classification until now. It attained very promising results on the datasets we tested it on: in our experiments based on a published dataset of response to drug therapy in Multiple Sclerosis patients, ECM used only an average of 40% of a time series and was able to outperform some of the baseline models, which needed the full time series for classification.

Early classification of time series has been receiving a lot of attention recently. In this paper we present a model, which we call the Early Classification Model (ECM), that allows for early, accurate and patient-specific classification of multivariate observations. ECM is comprised of an integration of the widely used Hidden Markov Model (HMM) and Support Vector Machine (SVM) models. It attained very promising results on the datasets we tested it on: in one set of experiments based on a published dataset of response to drug therapy in Multiple Sclerosis patients, ECM used only an average of 40% of a time series and was able to outperform some of the baseline models, which needed the full time series for classification. In the set of experiments tested on a sepsis therapy dataset, ECM was able to surpass the standard threshold-based method and the state-of-the-art method for early classification of multivariate time series.

Blockchains (BCs) are distributed database systems, popular for their innovative, unsupervised maintenance process. They use a so-called consensus protocol to prevent inference by any third party of absolute trust. Security, privacy, consistency, and energy consumption have been identified as the main issues involved in BC maintenance. According to the recent literature, some of these issues can be formulated as combinatorial optimization (CO) problems, and this fact motivated us to consider incorporating CO approaches into a BC. In this paper, we propose the new combinatorial optimization consensus protocol (COCP) based on the proof-of-useful-work (PoUW) concept that assumes solving instances of real-life CO problems. Due to the complexity of the underlying CO problems, we have developed various types of heuristic methods, which are utilized in the COCP. Most of these methods are problem-dependent stochastic heuristic or metaheuristic methods. As is the case with the majority of consensus protocols, PoUW exhibits the property of asymmetry. It is difficult to find a solution for the considered CO problem; however, once a solution is found, its verification is straightforward. We present here a BC framework combining the two above-mentioned fields of research: BC and CO. This framework consists of improvements aiming towards developing the COCP of the PoUW type. The main advantage of this consensus protocol is the efficient utilization of computing resources (by exploring them for finding solutions of real-life CO problem instances), and the provision of a broad range of incentives for the various BC participants. We enumerate the potential benefits of the COCP with respect to its practical impacts and savings in power consumption, describing in detail an illustrative example based on part of the real-life BC network. In addition, we identify several challenges that should be resolved in order to implement a useful, secure, and efficient PoUW consensus protocol.

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Meta-heuristics represent powerful tools for addressing hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in “reasonable” computing times. Moreover, a major issue in meta-heuristic design and calibration is to provide high performance solutions for a variety of problems. Parallel meta-heuristics aim to address both issues. The objective of this chapter is to present a state-of-the-art survey of the main parallelization ideas and strategies, and to discuss general design principles applicable to all meta-heuristic classes. To achieve this goal, the authors explain various paradigms related to parallel meta-heuristic development, where communications, synchronization, and control aspects are the most relevant. They also discuss implementation issues pointing out the characteristics of shared and distributed memory multiprocessors as target architectures. All these topics are illustrated by the examples from recent literature related to the parallelization of various meta-heuristic methods. Here, the authors focus on Variable Neighborhood Search and Bee Colony Optimization.