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检索条件"主题词=Evolutionary algorithm"

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Inventory Optimisation with an Interval Type-2 Fuzzy Model

Inventory Optimisation with an Interval Type-2 Fuzzy Model

引用

2010 IEEE World Congress on Computational Intelligence

作者： Miller, Simon Gongora, Mario John, Robert De Montfort Univ Ctr Computat Intelligence Leicester LE1 9BH Leics England

ISBN: (纸本)9781424469208

The planning of resources within a supply chain can prove to be a deciding factor in the success or failure of an operation. This research continues the authors' previous work using an extended Interval Type-2 Fuzzy Logic supply chain model, with an evolutionary algorithm to search for good resource plans. A set of enhanced experiments is conducted to validate our novel approach with optimal configurations, and determine an appropriate evolutionary algorithm set up for the given problem.

关键词： evolutionary algorithm evolutionary computation extended interval type-2 fuzzy logic supply chain model fuzzy set theory inventory management inventory optimisation optimisation resource planning supply chain management

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A new paradigm for photodynamic therapy: coherent control

A new paradigm for photodynamic therapy: coherent control

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12th World Congress of the International-Photodynamic-Association (IPA) on Photodynamic Therapy - Back to the Future

作者： Yang, Di Savolainen, Janne Jafarpour, Aliakbar Sprunken, Daan Herek, Jennifer L. Univ Twente Opt Sci Grp MESA Inst Nanotechnol NL-7500 AE Enschede Netherlands

ISBN: (纸本)9780819476609

Photodynamic therapy (PDT) is a treatment based on the interaction of light, photosensitizing agents and tissue oxygen. The light delivery in PDT is usually optimized by controlling the intensity, the spectrum, and/or the dosage of excitation light. In this paper, we introduce a novel method that aims to improve the efficiency of PDT by controlling the phase of the excitation light, an important and so far neglected parameter. This coherent control approach utilizes the coherence properties of light-matter interaction and aims to manipulate the quantum interferences between various available reaction pathways. In general, an outcome of a photochemical reaction can be optimized by enhancing the desired reaction pathways and suppressing other unwanted pathways. Such optimizations can be done by appropriate tailoring of the electric field profile of a broadband coherent excitation light, i.e. ultrafast laser pulse. Here, we used a femtosecond laser source with adaptive pulse shaping together with a molecular feedback in a learning loop to search for and synthesize such 'smart' laser pulses. Our control objective is to enhance the triplet yield of a model photosensitizer zinc phthalocyanine (ZnPc), which then leads to enhancement of the overall PDT process. We use two coherent control schemes where we optimize the ratio between the excited singlet state (S) and triplet state (T) ZnPc molecules both ways (S/T and T/S). We demonstrate a control of 15% over the triplet yield between the found best and the worst pulse shapes. Our preliminary results show that phase shaping can indeed be used in manipulating photosensitizer photophysics and correspondingly the yield of singlet oxygen.

关键词： femtosecond laser phase shaping coherent control zinc phthalocyanine learning-loop evolutionary algorithm

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Smart AC and Micro DC Grid Based DSM using Battery Storage and Wind Energy 2

Smart AC and Micro DC Grid Based DSM using Battery Storage a...

引用

2nd International Conference on Energy, Power and Environment - Towards Smart Technology (ICEPE)

作者： Babu, Naladi Ram Saikia, Lalit Chandra Saha, Debdeep NIT Silchar Dept Elect Engn Silchar India

ISBN: (纸本)9781538647691

Inadequate energy generation leads to unscheduled shedding of loads. Demand side management (DSM) finds a solution for it by shifting the controllable loads from peak hours to off-peak hours by active participation of customers in response to time of day tariff. This article evaluates the existing DSM for residential, commercial and industrial architecture by considering smart AC grid system and, smart AC grid and solar operated DC micro grid with battery storage system. The existing DSM structure is modified by further integrating generation from wind energy. The optimal load shifting for both existing and modified DSM architecture is done by using evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and hybrid particle swarm optimization (HPSO) with an objective to minimize the peak load demand which in turn reduces peak-to-average-ratio (PAR), energy bills and reshapes the load profile. The modified DSM architecture outperforms the existing one in terms of peak load demand reduction. Performance comparison among GA, PSO and HPSO witness the superiority of HPSO in terms of minimizing the peak load, PAR, energy bills and, reshaping the load profile.

关键词： AC smart grid Demand side management DC micro grid evolutionary algorithm TOD tariff wind energy

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GA strategies for optimal planning of daily energy consumptions and user satisfaction in buildings

GA strategies for optimal planning of daily energy consumpti...

引用

12th International Conference on Environment and Electrical Engineering (EEEIC)

作者： Pallotti, E. Mangiatordi, F. Fasano, M. Del Vecchio, P. Univ Roma TRE Dept Elect Rome Italy

ISBN: (纸本)9781467330596;9781467330602

The electricity demand in the residential sector in western countries is expected to grow in the next decade, partly as a result of the progressive deployment of electric vehicles. To deal with this problem without installing additional generation capacity, resulting in a loss of efficiency and increased greenhouse gas emissions, it is necessary to design a system for the optimal planning of electricity consumption in the residential sector. Since the energy use in buildings is closely related to the activities of residents, the shaping of the load curve and the smoothing of the energy peak must not be done without compromising the user comfort. This paper investigates the use of an heuristic strategies to find the optimal planning of energy consumption inside every building in a neighborhood. The issue is formulated as multi-objective optimization problem aiming at reducing the peak load as well as minimizing the energy cost and the impact on the user 'satisfaction. The effectiveness of the approach is confirmed by simulation results carried out on a residential area with a variety of electrical devices. The simulations reveal that the proposed strategy is able to plan the daily energy consumptions of a great number of electrical devices with good performance in terms of computational cost.

关键词： demand side management energy conpsuntion scheduling multiobjective optimizzation evolutionary algorithm

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SiblingRivalry: Online Autotuning Through Local Competitions

SiblingRivalry: Online Autotuning Through Local Competitions

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ACM International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)

作者： Ansel, Jason Pacula, Maciej Wong, Yee Lok Chan, Cy Olszewski, Marek O'Reilly, Una-May Amarasinghe, Saman MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9781450314244

Modern high performance libraries, such as ATLAS and FFTW, and programming languages, such as PetaBricks, have shown that autotuning computer programs can lead to significant speedups. However, autotuning can be burdensome to the deployment of a program, since the tuning process can take a long time and should be rerun whenever the program, microarchitecture, execution environment, or tool chain changes. Failure to re-autotune programs often leads to widespread use of sub-optimal algorithms. With the growth of cloud computing, where computations can run in environments with unknown load and migrate between different (possibly unknown) microarchitectures, the need for online autotuning has become increasingly important. We present SiblingRivalry, a new model for always-on online autotuning that allows parallel programs to continuously adapt and optimize themselves to their environment. In our system, requests are processed by dividing the available cores in half, and processing two identical requests in parallel on each half. Half of the cores are devoted to a known safe program configuration, while the other half are used for an experimental program configuration chosen by our self-adapting evolutionary algorithm. When the faster configuration completes, its results are returned, and the slower configuration is terminated. Over time, this constant experimentation allows programs to adapt to changing dynamic environments and often outperform the original algorithm that uses the entire system.

关键词： Autotuning evolutionary algorithm Genetic algorithm

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Design of a Krill Herd algorithm based Adaptive Channel Equalizer

Design of a Krill Herd Algorithm based Adaptive Channel Equa...

引用

International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Pandey, Saurav Patidar, Rohan George, Nithin V. Indian Inst Technol Guwahati Dept Chem Gauhati Assam India Indian Inst Technol Gandhinagar Dept Elect Engn Gandhinagar Gujarat India

ISBN: (纸本)9781479961207

This paper presents the design of an adaptive channel equalizer which is based on the recently proposed krill herd (KH) algorithm. The channel equalization problem, which is conventionally solved using gradient descent approaches, has been formulated as an evolutionary computing (EC) algorithm based optimization task. The performance of the proposed scheme has been compared with that obtained using other EC based equalizers and the KH algorithm based equalizer has been shown to offer improved channel equalization over the other schemes.

关键词： adaptive equalisers evolutionary computation gradient methods telecommunication channels EC based equalizer KH algorithm based equalizer Krill Herd algorithm based adaptive channel equalizer channel equalization evolutionary computing algorithm based optimization task gradient descent approache Adaptive equalizers algorithm design and analysis Bit error rate Optimization Signal processing algorithms Signal to noise ratio channel equalization adaptive equalisers algorithm design and analysis Signal processing algorithms signal to noise ratios Communication channels gradient methods evolutionary algorithm Bit error rate Equalizers HELIOS DEVICES Euphausiacea

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An Analysis of Artificial Immune System and Genetic algorithm in Urban Path Planning

An Analysis of Artificial Immune System and Genetic Algorith...

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36th Annual Conference of the IEEE Industrial-Electronics-Society (IECON)/4th IEEE International Conference on E-Learning in Industrial Electronics/IES Industry Forum

作者： Ojha, Unnati Chow, Mo-Yuen N Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781424452262

evolutionary algorithms like Genetic algorithm (GA) and Artificial Immune System (AIS) are commonly used to find solutions to problems not suitable for traditional optimization approaches. In this study, we compare the results of AIS and GA for path-planning where the objective is to optimize the safety and the travelling distance. Since these algorithms are computationally intensive, we perform offline optimization to generate a list of suboptimal solutions. Results show that the performance of GA and AIS are similar in terms of convergence and optimality. Furthermore, an analysis of AIS revealed that the convergence rate is faster at higher separation threshold;however, the effects of maturity age and percentage of hypermutation had minimal effects in convergence of AIS. Using AIS, we were also able to produce several sub-optimal paths in the form of memory cells, which provide robustness to the optimal path subject to perturbations.

关键词： artificial immune system artificial immune systems convergence of numerical methods convergence rate evolutionary algorithm genetic algorithm genetic algorithms optimization path planning transportation urban path planning

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Identification Control Synthesis By The Network Operator Method 10

Identification Control Synthesis By The Network Operator Met...

引用

10th IEEE Conference on Industrial Electronics and Applications

作者： Dang, T. P. Kazaryan, D. E. Diveev, A. I. Sofronova, E. A. PFUR Moscow Russia CC RAS Dorodnicyn Comp Ctr Inst Russian Acad Sci Moscow Russia

ISBN: (纸本)9781479983896

An identification control is a control of an object which mathematical model is unknown. For such objects initially the problem of identification is solved, and then for the resulting object model the problem of control synthesis is solved. As a result of control synthesis we obtain a multi-dimensional function that describes the dependence of control on the object state. After the implementation of this function in the control unit of the real object, it achieves the goal of control with optimal value of the quality criteria. To solve the identification and synthesis problems we use the method of network operator, which with the help of evolutionary algorithms finds the structure and parameters of the functions describing the model and functional dependence of control from the object state. In this paper, an example of using a network operator to solve the problem of identification control synthesis for a multilink robot is given.

关键词： Identification control synthesis evolutionary algorithm

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An immune algorithm for generalized rule induction

An immune algorithm for generalized rule induction

引用

CIE International Conference on Radar

作者： Zheng, HG Wang, L Jiao, LC Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shannxi Peoples R China

ISBN: (纸本)0780370007

Data mining usually means the efficient discovery of knowledge from databases, and the immune algorithm is a biological theory-based and globally searching algorithm. A specific immune algorithm is designed for discovering a few interesting, high-level prediction rules from databases, rather than discovering classification knowledge as usual in the literatures. Three important data mining issues addressed by the algorithm are the interest of the discovered knowledge, the computational efficiency of the algorithm, and the trade-off between expressiveness and efficiency.

关键词： data mining genetic algorithms knowledge representation computational complexity search problems generalized rule induction evolutionary algorithm genetic immune algorithm data mining database knowledge discovery biological theory-based algorithm globally searching algorithm high-level prediction rules classification knowledge discovery discovered knowledge interest algorithm computational efficiency tradeoff expressiveness

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CSS-A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization 1

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10th International Conference on Bioinspired Optimization Methods and Their Applications (BIOMA)

作者： Kong, Lingping Kumar, Abhishek Snasel, Vaclav Das, Swagatam Kromer, Pavel Ojha, Varun VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic Kyungpook Natl Univ Dept Artificial Intelligence Daegu 701702 South Korea Indian Stat Inst Elect & Commun Sci Unit 203 BT Rd Kolkata 700108 India Univ Reading Dept Comp Sci Reading England

ISBN: (数字)9783031210945

ISBN: (纸本)9783031210938;9783031210945

Due to the complex topology of the search space, expensive multi-objective evolutionary algorithms (EMOEAs) emphasize enhancing the exploration capability. Many algorithms use ensembles of surrogate models to boost the performance. Generally, the surrogate-based model either works out the solution's fitness by approximating the evaluation function or selects the solution by weighting the uncertainty degree of candidate solutions. This paper proposes a selection operator called Cheap surrogate selection (CSS) for multi-objective problems by utilizing the density probability on a k-dimensional tree. As opposed to the first type of surrogate models, which approximate the objective function, the proposed CSS only estimates the uncertainty of the candidate solutions. As a result, CSS does not require extensive sampling or training. Besides, CSS makes use of neighbors' density and builds the tree with low computational complexity, resulting in an accelerated surrogate process. Moreover, a new EMOEA is proposed by integrating spherical search as the core optimizer with the proposed selection scheme. Over a wide variety of benchmark problems, we show that the proposed method outperforms several state-of-the-art EMOEAs.

关键词： Multi-objective optimization evolutionary algorithm Cheap surrogate selection Spherical search

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