The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we propose a distributed planner method for multi-robot systems based on Swarm Intelligence. The method uses a distributed version of a priority based planner to compute coordinated motions of multiple robots in parallel. The Artificial Bee Colony algorithm is used to find velocity profiles that avoid collisions between robots and that minimize the time of the path execution. The proposed...
Vibration control of flexible structures has always been one of the most important issues and Among variant available control methods, active vibration control using piezoelectric sensors and actuators has become popular due to its high efficiency and flexibility for designing a control system. The main concern in designing a control system with piezoelectric patches is finding best position for patches...
Although several sequential heuristics have been proposed for dealing with the Unconstrained Binary Quadratic Programming (UBQP), very little effort has been made for designing parallel algorithms for the UBQP. This paper propose a novel decentralized parallel search algorithm, called Parallel Elite Biased Tabu Search (PEBTS). It is based on D2TS, a state-of-the-art sequential UBQP metaheuristic....
Multi-objective optimization (MOO) algorithms often use external archives to keep track of the Pareto-optimal solutions. Vector evaluated particle swarm optimization (VEPSO) is one such algorithm. In contrast to other MOO algorithms, VEPSO does not clearly define how to implement the archive. In this paper, the performance of various archive implementations, as found throughout the literature, are...
Multi-view learning is a novel paradigm that aims at obtaining better results by examining the information from several perspectives instead of by analysing the same information from a single viewpoint. The multi-view methodology has widely been used for semi-supervised learning, where just some patterns were previously classified by an expert and there is a large amount of unlabelled ones. However...
Several researches have pointed the hybridization of metaheuristics as an effective way to deal with combinatorial optimization problems. Hybridization allows the combination of different techniques, exploiting the strengths and compensating the weakness of each of them. MAHM is a promising adaptive framework for hybridization of metaheuristics, originally designed for single objective problems. This...
Data Envelopment Analysis (DEA) is a nonparametric methodology for estimating technical efficiency of a set of Decision Making Units (DMUs) from a dataset of inputs and outputs. This paper is devoted to computational aspects of DEA models under the application of the Principle of Least Action. This principle guarantees that the efficient closest targets are determined as benchmarks for each assessed...
Mutation testing is a method used to assess and improve the fault detection capability of a test suite by creating faulty versions, called mutants, of the system under test. Evolutionary Mutation Testing (EMT), like selective mutation or mutant sampling, was proposed to reduce the computational cost, which is a major concern when applying mutation testing. This technique implements an evolutionary...
This paper presents an empirical study comparing the performance of thirty-five boundary constraint-handling methods (BCHM) for PSO in constrained optimization, which were tested in a set of thirty-six well-known constrained problems. Each BCHM is composed as an hybrid consisting of one position update techniques and one velocity update strategy. Results show that the hybrid method that relocates...
Search-based software testing has achieved great attention recently, but the efficiency is still the bottleneck of it. This paper focuses on improving the efficiency of generating test data for multiple paths. Genetic algorithms are chosen as the heuristic algorithms in search-based software testing in this paper. First, we propose an improved grouping strategy of target paths to balance the load...
This paper investigates the capability of universal Kriging (UK), or Kriging with a trend, approximator enhanced with the efficient global optimization (EGO) method to solve expensive multi-objective design optimization problem. Engineering optimization problems typically can be well described with smooth and polynomial-like behavior, which is the main rationale to apply UK over the ordinary Kriging...
Those employing Evolutionary Algorithms (EA) are constantly challenged to engineer candidate solution representations that balance expressive power (I.E. can a wide variety of potentially useful solutions be represented?) and meta-heuristic search support (I.E. does the representation support fast acquisition and subsequent fine-tuning of adequate solution candidates). In previous work with a simulated...
Interactive multiobjective optimization (IMO) methods aim at supporting human decision makers (DMs) to find their most preferred solutions in solving multiobjective optimization problems. Due to the subjectivity of human DMs, human fatigue, or other limiting factors, it is hard to design experiments involving human DMs to evaluate and compare IMO methods. In this paper, we propose a framework of a...
Most published results show that power reduction of the finite-state machines (FSMs) is achieved by decomposition. In order to achieve a low power FSM implementation, a Genetic Fuzzy c-mean clustering-based decomposition method, called GFCM-D, is proposed for FSM partition in this study. GFCM-D used Fuzzy c-mean clustering (FCM) to partition a set of states of FSM into a collection of c fuzzy clusters,...
The mapping relation between decision variables and objective functions is complicated in multi-objective optimization problems. Dimension reduction-based memetic optimization strategy was proposed to decompose a multi-objective optimization problem into several easier subproblems in decision subspaces by detecting the correlation between decision variables and objective functions. In this work, the...
In the recent years, Large-Scale Global Optimization (LSGO) algorithms attempt to solve real-world problems efficiently. The imbalance in the contribution of variables and the interaction among variables pose major challenges for LSGO algorithms. This paper proposes mapping schemes based on the interaction among variables and the imbalance in the contribution of variables. The proposed mapping schemes...
The optimization of a model that expresses time series data for a given period is a problem associated with the development of a regression model that estimates future data on the extension of the past data time series. This is a two-step optimization problem where the order of past data used in the regression model (number of orders of the solution space) is decided, and weighted coefficients for...
Density based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance...
In Genetic Algorithms, the mutation operator is used to maintain genetic diversity in the population throughout the evolutionary process. Various kinds of mutation may occur over time, typically depending on a fixed probability value called mutation rate. In this work we make use of a novel data-science approach in order to adaptively generate mutation rates for each locus to the Neuroevolution of...
The test case generation of Cyber-Physical Systems (CPSs) face critical challenges that traditional methods such as Model-Based Testing cannot deal with. As a result, simulation-based testing is one of the most commonly used techniques for testing CPSs despite sometimes being computationally too expensive. This paper proposes a search-based approach which is implemented on top of Non-dominated Sorting...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.