In this paper a new approach to design sound phase diffusers is presented. The acoustic properties of such diffusers are usually increased by using single objective optimization methods. Here we propose the use of a m...
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In this paper a new approach to design sound phase diffusers is presented. The acoustic properties of such diffusers are usually increased by using single objective optimization methods. Here we propose the use of a multiobjective (MO) approach to design them in order to take into account several conflicting characteristic simultaneously. Three different MO problems are posed to consider various scenarios where fundamentally the objective is to maximize the normalized diffusion coefficient (following the corresponding Audio Engineering Society standard) for the so-called medium frequencies. This single objective could be divided into other several objectives to adjust performances to designer preferences. A multi-objective evolutionary algorithm (called ev-MOGA) is used to characterize the Pareto front in a smart way. ev-MOGA is modified, by using integer codification and tuning some of its genetic operators, to adapt it to the new requirements. Special interest is posed in selecting the diffusers codification properly to eliminate duplicities that would produce a multimodal problem. Precision in the manufacturing process is taking into account in the diffuser codification causing, that the number of different diffusers are quantified. Robust considerations related with the precision manufacturing process are considered in the decision making process. Finally, an optimal diffuser is selected considering designer preferences.
This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration (DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant of DG unit...
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This paper proposes a multi-objective evolutionary algorithm method for Distribution feeder reconfiguration (DFR) with distributed generators (DG) in a practical system. Considering the low inertia constant of DG units in order to take the transient stability of DGs into account is one of the major issues in power systems. Especially when the penetration of DGs is low, the impacts of them on the distribution system transient stability may be neglected. However, when the penetration of DG increases, the transient stability of them must be taken into account (more DGs, more transient issues). To this end, the DFR problem has been solve by an enhanced Gravitational Search Algorithm (EGSA) to improve the transient stability index and decrease losses and operation cost in a distribution test system with multiple micro-turbines. The effectiveness of the proposed approach is studied based on a typical 33-bus test system. For getting close to the practical condition and considering the detailed dynamic models of the generators and other electric devices in power system, simulation and programming of this approach are done by the DIgSILENT (R) Power Factory software. (C) 2015 Elsevier Ltd. All rights reserved.
This paper studies a family of redundant binary representations NNg(l, k), which are based on the mathematical formulation of error control codes, in particular, on linear block codes, which are used to add redundancy...
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This paper studies a family of redundant binary representations NNg(l, k), which are based on the mathematical formulation of error control codes, in particular, on linear block codes, which are used to add redundancy and neutrality to the representations. The analysis of the properties of uniformity, connectivity, synonymity, locality and topology of the NNg(l, k) representations is presented, as well as the way an (1+1)-ES can be modeled using Markov chains and applied to NK fitness landscapes with adjacent neighborhood. The results show that it is possible to design synonymously redundant representations that allow an increase of the connectivity between phenotypes. For easy problems, synonymously NNg(l, k) representations, with high locality, and where it is not necessary to present high values of connectivity are the most suitable for an efficient evolutionary search. On the contrary, for difficult problems, NNg(l, k) representations with low locality, which present connectivity between intermediate to high and with intermediate values of synonymity are the best ones. These results allow to conclude that NNg(l, k) representations with better performance in NK fitness landscapes with adjacent neighborhood do not exhibit extreme values of any of the properties commonly considered in the literature of evolutionary computation. This conclusion is contrary to what one would expect when taking into account the literature recommendations. This may help understand the current difficulty to formulate redundant representations, which are proven to be successful in evolutionary computation. (C) 2016 Elsevier B.V. All rights reserved.
The iterated prisoner's dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, eithe...
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The iterated prisoner's dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent's behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm;at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact nondeterministic finite state machines;they are extremely efficient in predicting opponents' replies, without being completely correct by necessity. Experimental results show that such a player is able to win several one-to-one games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes.
An optimization strategy combining computational fluid dynamics (CFD) with multiobjective evolutionary algorithm (MOEA) for dual-impeller design in an aerated tank was proposed to maximize the overall effective gas ho...
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An optimization strategy combining computational fluid dynamics (CFD) with multiobjective evolutionary algorithm (MOEA) for dual-impeller design in an aerated tank was proposed to maximize the overall effective gas holdup and minimize the power consumption with six geometrical variables. The nondominated sorting genetic algorithm-II (NSGA-II) was applied to construct a Pareto front from numerous design points with greatly reduced computation. The measurement of local gas holdup and power consumption by dual electric conductivity probe and torque sensor was utilized to verify the CFD model and evaluate the optimal design. The optimal design with a pitched concave blade disk turbine as the lower impeller and a down-pumping pitched blade turbine as the upper impeller exhibited the best gas dispersion performance with efficient energy savings. This approach has the potential to greatly enhance the efficiency of industrial stirred reactors.
Recently multi agent systems are used to solve complex problems. In these systems agents can cooperate when a problem is difficult or impossible to solve for an individual agent. Via learning, the agents attempt to ma...
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Recently multi agent systems are used to solve complex problems. In these systems agents can cooperate when a problem is difficult or impossible to solve for an individual agent. Via learning, the agents attempt to maximize some of their utilities. In multi agent learning an agent learns to interact with other agents and considering their behaviors. By multi task learning, the agent simultaneously learns a set of related problems and with reinforcement learning, an agent learns a proper policy to achieve its goal. In learning process, using the experience of teammate agents by simple interactions among them is very beneficial. In this paper we have presented a simple model of agents' interactions using operators of an evolutionary algorithm. Applying the proposed model has improved significantly the performance of multi task learning in a nondeterministic and dynamic environment, specifically for the dynamic maze problem. The experimental results indicate our claim.
We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a 87 rubidium Bose-Einstein condensate (BEC) can be divided into fundame...
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We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a 87 rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.
One of the most accurate types of prototype selection algorithms, preprocessing techniques that select a subset of instances from the data before applying nearest neighbor classification to it, are evolutionary approa...
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One of the most accurate types of prototype selection algorithms, preprocessing techniques that select a subset of instances from the data before applying nearest neighbor classification to it, are evolutionary approaches. These algorithms result in very high accuracy and reduction rates, but unfortunately come at a substantial computational cost. In this paper, we introduce a framework that allows to efficiently use the intermediary results of the prototype selection algorithms to further increase their accuracy performance. Instead of only using the fittest prototype subset generated by the evolutionary algorithm, we use multiple prototype subsets in an ensemble setting. Secondly, in order to classify a test instance, we only use prototype subsets that accurately classify training instances in the neighborhood of that test instance. In an experimental evaluation, we apply our new framework to four state-of-the-art prototype selection algorithms and show that, by using our framework, more accurate results are obtained after less evaluations of the prototype selection method. We also present a case study with a prototype generation algorithm, showing that our framework is easily extended to other preprocessing paradigms as well. (C) 2016 Elsevier B.V. All rights reserved.
We consider approximate controllability of semilinear non-autonomous evolutionary systems with nonlocal conditions. In this study, we use the theory of fractional powers and alpha-norms, so our results can be applied ...
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We consider approximate controllability of semilinear non-autonomous evolutionary systems with nonlocal conditions. In this study, we use the theory of fractional powers and alpha-norms, so our results can be applied to systems where nonlinear terms include derivatives of spatial variables. We formulate and prove sufficient conditions for approximate controllability. We also give a sample application of our results.
Optimization methods are widely used in computational models to improve the outcomes or tuning the model parameters, which often benefit human real life. This thesis introduces a new sampling technique called Opposite...
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Optimization methods are widely used in computational models to improve the outcomes or tuning the model parameters, which often benefit human real life. This thesis introduces a new sampling technique called Opposite-Center Learning (OCL) intended for convergence speed-up of meta-heuristic optimization algorithms. The simple version of OCL, 1-1 OCL, comprises an extension of Opposition-Based Learning (OBL), a sim- ple scheme that manages to boost numerous optimization methods by considering the opposite points of candidate solutions. In contrast to OBL, 1-1 OCL has a theoretical foundation – the opposite-center point is defined as the optimal choice in pair-wise sam- pling of the search space given a random starting point. A concise analytical background is provided. Based on the research of 1-1 OCL, m-n OCL is developed so that OCL can generate n points from m known points and grant their optimality in the sense of all m and n points generated by m-n OCL scheme having shorter expected distances to an ar- bitrary distributed global optimum. Computationally both the opposite-center point in 1- 1 OCL and opposite-center points in m-n OCL are approximated by a lightweight Monte Carlo scheme for arbitrary dimension. Empirical results up to dimension 20 confirm that 1-1 OCL outperforms OBL and random sampling: the points generated by OCL have shorter expected distances to a uniformly distributed global optimum, where m-n OCL does even better. To further test its practical performance, both 1-1 OCL and m-n OCL are applied to differential evolution (DE). This novel scheme for continuous optimization named Opposite-Center DE (OCDE) and m-n Opposite-Center DE (MNOCDE), they employ OCL for population initialization and generation jumping. Numerical experi- ments on a set of benchmark functions for dimensions 10, 30, 50 and 100 reveal that OCDE and MNOCDE on average improves the convergence rates compared to the orig- inal DE and the Opposition-based DE (ODE), respectively, w
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