The advancement of cloud computing has enabled workflow scheduling to provide users with more network resources. However, there are some scheduling issues between resource allocation and user needs in workflows in Iaa...
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The advancement of cloud computing has enabled workflow scheduling to provide users with more network resources. However, there are some scheduling issues between resource allocation and user needs in workflows in IaaS environments. Based on this, this study adopts a heuristic scheduling model based on deadline and list and constructs a single objective workflow scheduling model based on deadline. Based on fuzzy-dominated sorting, traditional non-dominated sorting is improved to construct a time-cost dual objective workflow scheduling model. Introducing evolutionary algorithms with a reliability index as the scheduling objective, a time-cost reliability three-objective workflow scheduling model is constructed. The results showed that the total execution time of the single objective workflow scheduling model in four standard workflows was 92s, 106s, 113s, and 105s, respectively. The throughput was 144b/s, 138b/s, 140b/s, and 142b/s, respectively, all of which were superior to other models. Compared with other comparative models, the dual objective workflow scheduling model and the three objective workflow scheduling model had higher HV values, less execution time, and better Pareto frontier solutions. This study solves the three objective scheduling problem of time cost reliability in IaaS environment, which has a certain reference value in resource scheduling on cloud platforms.
Blockchain systems are prone to concurrency bugs due to the nondeterminism in the delivery order of messages between the distributed nodes. These bugs are hard to detect since they can only be triggered by a specific ...
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ISBN:
(纸本)9798350300376
Blockchain systems are prone to concurrency bugs due to the nondeterminism in the delivery order of messages between the distributed nodes. These bugs are hard to detect since they can only be triggered by a specific order or timing of concurrent events in the execution. Systematic concurrency testing techniques, which explore all possible delivery orderings of messages to uncover concurrency bugs, are not scalable to large distributed systems such as blockchains. Random concurrency testing methods search for bugs in a randomly generated set of executions and offer a practical testing method. In this paper, we investigate the effectiveness of random concurrency testing on blockchain systems using a case study on the XRP Ledger of the Ripple blockchain, which maintains one of the most popular cryptocurrencies in the market today. We test the Ripple consensus algorithm of the XRP Ledger by exploring different delivery orderings of consensus protocol messages. Moreover, we design an evolutionary algorithm to guide the random test case generation toward certain system behaviors to discover concurrency bugs more efficiently. Our case study shows that random concurrency testing is effective at detecting concurrency bugs in blockchains, and the evolutionary approach for test generation improves test efficiency. Our experiments could successfully detect the bugs we seeded in the Ripple source code. Moreover, we discovered a previously unknown concurrency bug in the production implementation of Ripple.
evolutionary algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., ine...
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ISBN:
(纸本)9781450328814
evolutionary algorithms (EAs), when used for global optimization, can be seen as unconstrained optimization techniques. Therefore, they require an additional mechanism to incorporate constraints of any kind (i.e., inequality, equality, linear, nonlinear) into their fitness function. Although the use of penalty functions (very popular with mathematical programming techniques) may seem an obvious choice, this sort of approach requires a careful fine tuning of the penalty factors to be used. Otherwise, an EA may be unable to reach the feasible region (if the penalty is too low) or may reach quickly the feasible region but being unable to locate solutions that lie in the boundary with the infeasible region (if the penalty is too severe). This has motivated the development of a number of approaches to incorporate constraints into the fitness function of an EA. This tutorial will cover the main proposals in current use, including novel approaches such as the use of tournament rules based on feasibility, multiobjective optimization concepts, hybrids with mathematical programming techniques (e.g., Lagrange multipliers), cultural algorithms, and artificial immune systems, among others. Other topics such as the importance of maintaining diversity, current benchmarks and the use of alternative search engines (e.g., particle swarm optimization, differential evolution, evolution strategies, etc.) will be also discussed (as time allows).
To solve noisy and expensive multi-objective optimization problems, there are only a few function evaluations can be used due to the limitation of time and/or money. Because of the influence of noises, the evaluations...
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To solve noisy and expensive multi-objective optimization problems, there are only a few function evaluations can be used due to the limitation of time and/or money. Because of the influence of noises, the evaluations are inaccurate. It is challenging for the existing surrogate-assisted evolutionary algorithms. Due to the influence of noises, the performance of the surrogate model constructed by these algorithms is degraded. At the same time, noises would mislead the evolution direction. More importantly, because of the limitations of function evaluations, noise treatment methods consuming many function evaluations cannot be applied. An adaptive model switch-based surrogate-assisted evolutionary algorithm is proposed to solve such problems in this paper. The algorithm establishes radial basis function networks for denoising. An adaptive model switch strategy is adopted to select suited surrogate model from Gaussian regression and radial basis function network. It adaptively selects the sampling strategies based on the maximum improvement in the convergence, diversity, and approximation uncertainty to make full use of the limited number of function evaluations. The experimental results on a set of test problems show that the proposed algorithm is more competitive than the five most advanced surrogate-assisted evolutionary algorithms.
Recently, the efficient deployment of wireless sensor networks (WSNs) has become a leading field of research in WSN design optimization. Practical scenarios related to WSN deployment are often considered as optimizati...
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Recently, the efficient deployment of wireless sensor networks (WSNs) has become a leading field of research in WSN design optimization. Practical scenarios related to WSN deployment are often considered as optimization models with multiple conflicting objectives that are simultaneously enhanced. In the related literature, it had been shown that moving from mono-objective to multi-objective resolution of WSN deployment is beneficial. However, since the deployment of real-world WSNs encompasses more than three objectives, a multi-objective optimization may harm other deployment criteria that are conflicting with the already considered ones. Thus, our aim is to go further, explore the modeling and the resolution of WSN deployment in a many-objective (i.e., optimization with more than three objectives) fashion and especially, exhibit its added value. In this context, we first propose a many-objective deployment model involving seven conflicting objectives, and then we solve it using an adaptation of the Decomposition-based evolutionary Algorithm " theta-DEA". The developed adaptation is named "WSN-theta-DEA" and is validated through a detailed experimental study.
Through System Identification techniques, it is possible to obtain a mathematical model for a dynamic system from its input/output data. Due to their intrinsic dynamic behavior and simple and fast training procedure, ...
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Through System Identification techniques, it is possible to obtain a mathematical model for a dynamic system from its input/output data. Due to their intrinsic dynamic behavior and simple and fast training procedure, the use of echo state networks (ESNs), a kind of neural network, for System Identification is advantageous. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, a random reservoir may not be ideal in terms of performance. Due to their theoretical ability to obtain good solutions with few evaluations, the Real Coded Quantum-Inspired evolutionary Algorithm (QIEA-R) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) represent efficient alternatives of evolutionary algorithms for optimizing ESN global parameters and weights. Thus, this work proposes a neuro-evolutionary method that automatically defines an ESN for System Identification problems. The method initially focuses on finding the best ESN global parameters by using the QIEA-R or the CMA-ES then, in sequence, selecting its best reservoir, which can be done by a second optimization focused on some reservoir weights or by doing a simple choice based on networks with random reservoirs. The method was applied to seven benchmark problems in System Identification produced good results when compared to traditional methods.
Generating diverse populations of high-quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research with an investigation on evol...
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Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strategy is to focus on exploration, hopeful...
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Learning optimal policies in sparse rewards settings is difficult as the learning agent has little to no feedback on the quality of its actions. In these situations, a good strategy is to focus on exploration, hopefully leading to the discovery of a reward signal to improve on. A learning algorithm capable of dealing with this kind of setting has to be able to (1) explore possible agent behaviors and (2) exploit any possible discovered reward. Exploration algorithms have been proposed that require the definition of a low-dimension behavior space, in which the behavior generated by the agent's policy can be represented. The need to design a priori this space such that it is worth exploring is a major limitation of these algorithms. In this work, we introduce STAX, an algorithm designed to learn a behavior space on-the-fly and to explore it while optimizing any reward discovered (see Figure 1). It does so by separating the exploration and learning of the behavior space from the exploitation of the reward through an alternating two-step process. In the first step, STAX builds a repertoire of diverse policies while learning a low-dimensional representation of the high-dimensional observations generated during the policies evaluation. In the exploitation step, emitters optimize the performance of the discovered rewarding solutions. Experiments conducted on three different sparse reward environments show that STAX performs comparably to existing baselines while requiring much less prior information about the task as it autonomously builds the behavior space it explores.
The gannet optimization algorithm (GOA) is an effective group intelligence algorithm inspired by the foraging behavior of gannets. Despite its merits, considerable potential exists for enhancing its exploration and co...
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JMI Editor in Chief Bennett Landman provides an overview of JMI Volume 12 Issue 1 and spotlights key aspects of JMI peer review, with an eye toward continued growth for the JMI community.
JMI Editor in Chief Bennett Landman provides an overview of JMI Volume 12 Issue 1 and spotlights key aspects of JMI peer review, with an eye toward continued growth for the JMI community.
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