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In Systems-on-Chip (SoCs) based on Networks-on-Chip (NoCs), the timing requirements of target applications can be met by using virtual channels and traffic differentiation mechanisms to prioritize the most urgent communication streams. However, the use of virtual channels in NoCs results in silicon and power overheads as they are usually implemented by means of additional buffers and multiplexers...
In the Team Orienteering Problem (TOP) a set of locations is given, each with a score. The objective is to determine a fixed number of routes (teams), limited in length, that visit some locations and maximize the sum of the collected scores. For the first time we introduce bi-objective TOP which has a second objective, to balance all team's scores for the purpose of obtaining fair teams. So the second...
Hyper-heuristics have emerged as an important strategy for combining the strengths of different heuristics into a single method. Although hyper-heuristics have been found to be successful in many scenarios, little attention has been paid to the subsets of heuristics that these methods manage and apply. In several cases, heuristics can interfere with each other and can be harmful for the search. Thus,...
We present a method for the design optimization of a soft, inflatable robot. The method described utilizes a multi-objective fitness function together with custom, platform-specific metrics related to the dexterity and load-bearing capacity of inflatable manipulators. Candidate designs are scored by computing these metrics at many randomly generated configurations and then by appropriately combining...
Heating, Ventilation, and Air Conditioning (HVAC) systems account for a large share of the energy consumed in commercial buildings. Simple strategies such as adjusting HVAC set point temperatures can lead to significant energy savings at no additional financial costs. Despite their promising results, it is currently unclear if such operation strategies can have unintended consequences on other building...
The process model matching i.e., finding correspondences between activities of process models, is a crucial task in many activities of business process management lifecycle such as merging, clustering or querying business processes. This paper presents an approach to solve the problem of process model matching. The proposed approach formulates the problem as an optimization problem and uses a genetic...
With the growing amount of documents in the search index of information retrieval systems, the problem of ranking documents becomes crucial. The modern state of the problem leads to the point where machine learning becomes the most efficient way to optimize the ranking function. In this article investigated ranking function in information retrieval systems (IRS) and learning to rank problem. During...
The software testing is an important component of software development life cycle, that directly affects quality of software products. Some problems in software testing phase can't be optimized only with traditional Software Engineering techniques. It is possible to do the mathematical modelling of those problems in an attempt to optimize them through the search techniques. This study presents a framework...
Artificial bee colony (ABC) algorithm is a novel optimizer which simulates bees' foraging behavior in solution space for finding global optimal solution with reasonable criteria. In this paper, an improved artificial bee colony is proposed to solve multi-objective (MO) problems. In order to guide bees toward to Pareto optimal solutions, bees' moving behavior is modified to make ABC capable for solving...
Multi-objective optimization problems with interval (MOPs-I) uncertainties parameters are common in practice. Evolutionary multi-objective (EMO) algorithms are popularly employed to solve these problems due to their powerful explorations. The comparison strategies among interval objectives of MOPs-I are crucially important for obtaining a superior Pareto front when applying EMOs. By effectively combining...
In real world optimization problems there are often multiple objectives to consider. However with regular multiobjective genetic algorithms the more objectives there are the more of a problem this becomes for the Pareto front. This is why solutions for Many Objective Problems should be explored. Many objective problems differ from multi-objective problems in that they have more than three objectives...
This paper proposes a method for tuning compilations to improve the size, execution time and reliability of the final application altogether. Our approach implements a genetic strategy with a multi-objective evolution that takes advantage of the NSGA-II algorithm for selecting the best compilations. Experiments show that reliability can be improved by efficiently exploring the compiler optimization...
This paper focuses on coverage sampling over a given region by a fleet of underwater gliders. The coverage performance is evaluated by the observing uncertainty performed by Objective Analysis (OA). Deployed gliders are commonly restricted to move through transects along which the data collected are convenient for data assimilation. We optimize the initial location and the initial heading of each...
Resistive switching property enables various promising applications such as design of non-volatile in-memory computing devices which has attracted high attention to Resistive Random Access Memories (RRAMs). In this work, we present a multi-objective BDD optimization approach for RRAM based logic circuit design. Dissimilar to classical BDD optimization, evaluating the cost metrics of the circuits in...
Genetic algorithm (GA) is widely used in the electric energy systems. GA can serve to find solution of optimization problem with the reasonable time and resources. GA is defined with parameters and selection criteria. The dispatcher receives the results of the optimization with using GA for the subsequent analysis and using in electric energy system. In recent years the mathematicians achieved the...
Geolocation of wireless emitters using Received Signal Strength Difference (RSSD) and site-specific propagation models offers the advantages of increased accuracy and operation with non-cooperative emitters (where emitted power level or positional information such as GPS from the emitter is unavailable) over more commonplace Received Signal Strength (RSS) systems. An important factor influencing the...
This paper proposes a new method for automation and optimization of coverage-driven verification (CDV) of hardware systems that is based on evolutionary computing. In comparison with the standard CDV that utilizes random search, using this method, the convergence to the maximum coverage is much faster, fewer input stimuli are used and no manual effort is required from the user. Moreover, the optimization...
A new genetic algorithm to search for Pareto-optimal solutions in multi-objective problems with constraints is proposed. This algorithm employs the parallel evaluation strategy in which feasible and infeasible solutions are preserved in separate populations. Feasible solutions are ranked in accordance with the ordinary non-dominated ranking method. On the other hands, infeasible solutions are ranked...
In this paper, a heuristic, based on the non dominated sorting genetic algorithm II (NSGA II), is developed to exploit a known fuzzy outranking relation, with the purpose of constructing a recommendation for a medium-sized multicriteria ranking problem. The performance of the proposed evolutionary algorithm is evaluated on a real medium-sized problem. The results indicate that the proposed evolutionary...
Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering...
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