Extracting important statistical patterns from wind speed time series at different time scales is of interest to wind energy industry in terms of wind turbine optimal control, wind energy dispatch/scheduling, wind ene...
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Extracting important statistical patterns from wind speed time series at different time scales is of interest to wind energy industry in terms of wind turbine optimal control, wind energy dispatch/scheduling, wind energy project design and assessment, and so on. In this paper, a systematic way is presented to estimate the first order (one step) Markov chain transition matrix from wind speed time series by two steps. Wind speed time series data is used first to generate basic estimators of transition matrices (i.e. first order, second order, third order, etc.) based on counting techniques. Then an evolutionary algorithm (EA), specifically double-objective evolutionary strategy algorithm (ES), is proposed to search for the first order Markov chain transition matrix which can best match these basic estimators after transforming the first order transition matrix into its higher order counterparts. The evolutionary search for the first order transition matrix is guided by a predefined cost function which measures the difference between the basic estimators and the first order transition matrix, and its high order transformations. To deal with the potential high dimensional optimization problem (i.e. large transition matrices), an enhanced offspring generation procedure is proposed to help the ES algorithm converge efficiently and find better Pareto frontiers through generations. The proposed method is illustrated with wind speed time series data collected from individual 1.5 MW wind turbines at different time scales. (C) 2011 Elsevier Ltd. All rights reserved.
Cognitive radio has been regarded as a promising technology to improve spectrum utilization significantly. Many studies have discussed underlay spectrum sharing and power control, but issues such as the interference o...
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Cognitive radio has been regarded as a promising technology to improve spectrum utilization significantly. Many studies have discussed underlay spectrum sharing and power control, but issues such as the interference of the primary system have just been considered as the constraint. In this paper, we build a spectrum allocation mathematical model which considers different interference intensity according to relative geographic locations between two SLs in the spectrum-sharing mode of cognitive radio network. Then it's converted into multi-objective optimization problem. To solve the spectrum sharing problem, the multi-objective improved genetic algorithm is adopted. Simulation results show that our proposed methods greatly outperform the commonly used K-max-cut in graph theory. It can better realize the network benefit maximization and reduce the disturbance to the primary system by using the multi-objective optimization algorithm.
Search-based statistical structural testing(SBSST)is a promising technique that uses automated search to construct input distributions for statistical structural *** has been proved that a simple search algorithm,for ...
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Search-based statistical structural testing(SBSST)is a promising technique that uses automated search to construct input distributions for statistical structural *** has been proved that a simple search algorithm,for example,the hill-climber is able to optimize an input ***,due to the noisy fitness estimation of the minimum triggering probability among all cover elements(Tri-Low-Bound),the existing approach does not show a satisfactory *** input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation ***-Low-Bound is considered a strong criterion,and it is demonstrated to sustain a high fault-detecting *** article tries to answer the following question:if we use a relaxed constraint that significantly reduces the time consumption on search,can the optimized input distribution still be effective in faultdetecting ability?In this article,we propose a type of criterion called fairnessenhanced-sum-of-triggering-probability(p-L1-Max).The criterion utilizes the sum of triggering probabilities as the fitness value and leverages a parameter p to adjust the uniformness of test data *** conducted extensive experiments to compare the computation time and the fault-detecting ability between the two *** result shows that the 1.0-L1-Max criterion has the highest efficiency,and it is more practical to use than the Tri-Low-Bound *** measure a criterion’s fault-detecting ability,we introduce a definition of expected faults found in the effective test set size *** measure the effective test set size region,we present a theoretical analysis of the expected faults found with respect to various test set sizes and use the uniform distribution as a baseline to derive the effective test set size region’s definition.
The aeronautical industry is still under expansion in spite of the problems it is facing due to the increase in oil prices, limited capacity, and novel regulations. The expansion trends translate into problems at diff...
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The aeronautical industry is still under expansion in spite of the problems it is facing due to the increase in oil prices, limited capacity, and novel regulations. The expansion trends translate into problems at different locations within an airport system and are more evident when the resources to cope with the demand are limited or are reaching to theirs limits. In the check-in areas they are appreciated as excessive waiting times which in turn are appreciated by the customers as bad service levels. The article presents a novel methodology that combines an evolutionary algorithm and simulation in order to give the best results taking into account not only the mandatory hard and soft rules determined by the internal policies of an airport terminal but also the quality indicators which are very difficult to include using an abstract representation. The evolutionary algorithm is developed to satisfy the different mandatory restrictions for the allocation problem such as minimum and maximum number of check-in desks per flight, load balance in the check-in islands, opening times of check-in desks and other restrictions imposed by the level of service agreement. Once the solutions are obtained, a second evaluation is performed using a simulation model of the terminal that takes into account the stochastic aspects of the problem such as arriving profiles of the passengers, opening times physical configurations of the facility among other with the objective to determine which allocation is the most efficient in real situations in order to maintain the quality indicators at the desired level. (C) 2015 Elsevier Ltd. All rights reserved.
Harmony search is a powerful metaheuristic algorithm with excellent exploitation capabilities but suffers a very serious limitation of premature convergence if one or more initially generated solutions/harmonies are i...
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Harmony search is a powerful metaheuristic algorithm with excellent exploitation capabilities but suffers a very serious limitation of premature convergence if one or more initially generated solutions/harmonies are in the vicinity of local optimal. In order to remove this limitation this paper proposes a novel algorithm based on hybridization of Harmony search and Simulated Annealing called HS-SA to inherit their advantages in a complementary way. Taking the inspiration from Simulated Annealing the proposed HS-SA algorithm accepts even the inferior harmonies with a probability determined by parameter called Temperature. The Temperature parameter is initially kept high to favor exploration of search space and is linearly decreased to gradually shift focus to exploitation of promising search areas. The performance of HS-SA is tested on IEEE CEC 2014 benchmark functions and real life problem from computer vision called Camera Calibration problem. The numerical results demonstrate the superiority of the proposed algorithm. (C) 2018 Elsevier Inc. All rights reserved.
Template matching (TM) plays an important role in several image-processing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the best-possible re...
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Template matching (TM) plays an important role in several image-processing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the best-possible resemblance between a subimage known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method aims for the best-possible coincidence between the images through an exhaustive computation of the normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). Recently, several TM algorithms that are based on evolutionary approaches have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the electromagnetism-like algorithm (EMO) is proposed to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version, which incorporates a modification of the local search procedure to accelerate the exploitation process. As a result, the new EMO algorithm can substantially reduce the number of fitness function evaluations while preserving the good search capabilities of the original EMO. In the proposed approach, particles represent search locations, which move throughout the positions of the source image. The NCC coefficient, considered as the fitness value (charge extent), evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (particle). The number of NCC evaluations is also reduced by considering a memory, which stores the NCC values previously visited to avoid the re-evaluation of the same search locations (particles). Guided by the fitness values (NCC coefficients), the set of candidate positions are evolved through EMO operators until the best-possible resemblance is determine
Feature selection (FS) is considered as one of the most common and challenging tasks in Machine Learning. FS can be considered as an optimization problem that requires an efficient optimization algorithm to find its o...
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Feature selection (FS) is considered as one of the most common and challenging tasks in Machine Learning. FS can be considered as an optimization problem that requires an efficient optimization algorithm to find its optimal set of features. This paper proposes a wrapper FS method that combines a time-varying number of leaders and followers binary Salp Swarm Algorithm (called TVBSSA) with Random Weight Network (RWN). In this approach, the TVBSSA is used as a search strategy, while RWN is utilized as an induction algorithm. The objective function is formulated in a manner to aggregate three objectives: maximizing the classification accuracy, maximizing the reduction rate of the selected features, and minimizing the complexity of generated RWN models. To assess the performance of the proposed approach, 20 well-known UCI datasets and a number of existing FS methods are employed. The comparative results show the ability of the proposed approach in outperforming similar algorithms in the literature and its merits to be used in systems that require FS. (C) 2019 Elsevier Ltd. All rights reserved.
A shift from even-aged forest management to uneven-aged management practices leads to a problem rather different from the existing straightforward practice that follows a rotation cycle of artificial regeneration, thi...
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A shift from even-aged forest management to uneven-aged management practices leads to a problem rather different from the existing straightforward practice that follows a rotation cycle of artificial regeneration, thinning of inferior trees and a clearcut. A lack of realistic models and methods suggesting how to manage uneven-aged stands in a way that is economically viable and ecologically sustainable creates difficulties in adopting this new management practice. To tackle this problem, we make a two-fold contribution in this paper. The first contribution is the proposal of an algorithm that is able to handle a realistic uneven-aged stand management model that is otherwise computationally tedious and intractable. The model considered in this paper is an empirically estimated size-structured ecological model for uneven-aged spruce forests. The second contribution is on the sensitivity analysis of the forest model with respect to a number of important parameters. The analysis provides us an insight into the behavior of the uneven-aged forest model. (C) 2016 Elsevier B.V. All rights reserved.
Lightweight bistable deployable structures can be designed to be transportable and reusable. They instantaneously achieve some structural stability when transformed from the compact to the deployed state through a con...
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Lightweight bistable deployable structures can be designed to be transportable and reusable. They instantaneously achieve some structural stability when transformed from the compact to the deployed state through a controlled snap-through, as a result of intended geometric incompatibilities between the beams. Due to their transformable bistable nature their design requires assessing both their non-linear transformation behaviour, as well as their service state in the deployed configuration. The requirement of a low peak force during transformation can be shown to oppose the high stiffness requirement in the deployed state;their design can therefore be formulated as a multi-objective non-linear optimisation problem. In this contribution, a size and shape optimisation method is elaborated by choosing the best material combinations, the optimal geometry of the structure and beam cross-sections. The originality of this contribution is the use of a multi-objective evolutionary algorithm to structurally optimise bistable scissor structures taking into account the deployed state as well as the transformation phase. First, the method is applied to optimise a single bistable scissor module. Next, a multi-module bistable scissor structure is optimised and the single module and full structure based approaches are critically compared.
Cultural algorithms (CA) use social intelligence to solve problems in optimization. The CA is a class of evolutionary computational models inspired from observing the cultural evolutionary process in nature. Cultural ...
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Cultural algorithms (CA) use social intelligence to solve problems in optimization. The CA is a class of evolutionary computational models inspired from observing the cultural evolutionary process in nature. Cultural algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. Knowledge from these sources is then combined to influence the decisions of the individual agents in solving problems. Classification using "IF-THEN" rules comes under descriptive knowledge discovery in data mining and is the most sought out by users since they represent highly comprehensible form of knowledge. The rules have certain properties which make them useful forms of actionable knowledge to the users. The rules are evaluated using these properties represented as objective and subjective measures. The rule properties may be conflicting. Hence discovery of rules with specific properties is considered as a multi-objective optimization problem. In the current study an extended cultural algorithm which applies social intelligence in the data mining domain to present users with a set of rules optimized according to user specified metrics is proposed. Preliminary experimental results using benchmark data sets reveal that the algorithm is promising in producing rules with specific properties.
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