Global optimisation method Differential Migration (DM) with restarting is described in this paper and evaluated together with Restart Covariance Matrix Adaptation Evolution Strategy With Increasing Population Size (IP...
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ISBN:
(纸本)9781509064359
Global optimisation method Differential Migration (DM) with restarting is described in this paper and evaluated together with Restart Covariance Matrix Adaptation Evolution Strategy With Increasing Population Size (IPOP-CMA-ES). Differential Migration is another step in global optimisation from SOMA (Self-Organizing Migration Algorithm) combining two basic individual movement methods of SOMA - all-to-one and all-to-all, via cluster analysis and internal algorithm constant defining continuous change from one type of movement to another. The proposed algorithm implements essential ideas of Differential Evolution regardless of their original interpretation in living nature with subsequent increase of efficiency in finding global extreme which holds mainly for noisy multimodal cost functions present in the benchmarks as well as in real world applications.
This paper addresses the dimensional-synthesis-based kineto-elastostatic performance optimization of the DELTA parallel mechanism. For the manipulator studied here, the main consideration for the optimization criteria...
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This paper addresses the dimensional-synthesis-based kineto-elastostatic performance optimization of the DELTA parallel mechanism. For the manipulator studied here, the main consideration for the optimization criteria is to find the maximum regular workspace where the robot DELTA must posses high stiffness and dexterity. The dexterity is a kinetostatic quality measure that is related to joint's stiffness and control accuracy. In this study, we use the Castigliano's energetic theorem for modeling the elastostatic behavior of the DELTA parallel robot, which can be evaluated by the mechanism's response to external applied wrench under static equilibrium. In the proposed formulation of the design problem, global structure's stiffness and global dexterity are considered together for the simultaneous optimization. Therefore, we formulate the design problem as a multi-objective optimization one and, we use evolutionary genetic algorithms to find all possible trade-offs among multiple cost functions that conflict with each other. The proposed design procedure is developed through the implementation of the DELTA robot and, numerical results show the effectiveness of the proposed design method to enhancing kineto-elastostatic performance of the studied manipulator's structure.
This paper presents a simple and efficient real-coded genetic algorithm (RCGA) for constrained real-parameter optimization. Different from some conventional RCGAs that operate evolutionary operators in a series framew...
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This paper presents a simple and efficient real-coded genetic algorithm (RCGA) for constrained real-parameter optimization. Different from some conventional RCGAs that operate evolutionary operators in a series framework, the proposed RCGA implements three specially designed evolutionary operators, named the ranking selection (RS), direction-based crossover (DBX), and the dynamic random mutation (DRM), to mimic a specific evolutionary process that has a parallel-structured inner loop. A variety of benchmark constrained optimization problems (COPs) are used to evaluate the effectiveness and the applicability of the proposed RCGA. Besides, some existing state-of-the-art optimization algorithms in the same category of the proposed algorithm are considered and utilized as a rigorous base of performance evaluation. Extensive comparison results reveal that the proposed RCGA is superior to most of the comparison algorithms in providing a much faster convergence speed as well as a better solution accuracy, especially for problems subject to stringent equality constraints. Finally, as a specific application, the proposed RCGA is applied to optimize the GaAs film growth of a horizontal metal-organic chemical vapor deposition reactor. Simulation studies have confirmed the superior performance of the proposed RCGA in solving COPs. (C) 2015 Elsevier B.V. All rights reserved.
Process planning and scheduling (PPS) problem is a most complicated and practical scheduling problem in intelligent manufacturing systems. PPS processes a set of prismatic parts into completed products effectively and...
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Process planning and scheduling (PPS) problem is a most complicated and practical scheduling problem in intelligent manufacturing systems. PPS processes a set of prismatic parts into completed products effectively and economically by determining the optimal process plans and the most appropriate moment to execute each operation with competitive resources. Many research works use multiobjective evolutionary algorithm (MOEA) to solve such problems. This paper proposes an improved hybrid sampling strategy-based multiobjective evolutionary algorithm (HSS-MOEA) combining with differential evolution (HSS-MOEA-DE) to solve the PPS problem. HSS-MOEA-DE divides population and elitism into two parts: center and edge individuals closing to Pareto frontier by special selection strategies according to their objectives and special designed fitness function. Moreover, differential evolution tries to improve the convergence and distribution performances of individuals by converging to the different directions of Pareto frontier through sampling randomly from different parts, in which can be easily distinguished the relationships between dominated and dominating individuals. Numerical comparisons show that the HSS-MOEA-DE outperforms the traditional HSS-MOEA without DE in convergence and distribution performances.
The grid is hierarchical in three parts whose functions are quite different. First, the transmission system's role is to transport energy into high voltage from production centers to consumption areas and distribu...
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ISBN:
(纸本)9781509042371
The grid is hierarchical in three parts whose functions are quite different. First, the transmission system's role is to transport energy into high voltage from production centers to consumption areas and distribution network directly supplies large industrial consumers and supplying the average and low voltage consumers, with the latest advances in industry and Technological the need for electricity is increasing. These developments require the development of distribution network to optimize the balance between supply and demand. A very large part of the power losses and deviation voltage problems in power systems are assigned to distribution network, where power losses are estimated at 14 %. So, this paper investigates the use of the multi objective Differential Evolution algorithms developed by Matlab to find the optimal distribution network reconfiguration minimizing power losses and improve voltage deviation. To test the effectiveness of the proposed algorithm, three IEEE distribution systems respectively 33 buses, 69 busses and 123 busses are used for two objectives functions and the results are being compared.
The Test Suite Minimization Problem (TSMP) is a NP-hard real-world problem that arises in the field of software engineering. It consists in selecting a minimal set of test cases from a large test suite, ensuring that ...
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The Test Suite Minimization Problem (TSMP) is a NP-hard real-world problem that arises in the field of software engineering. It consists in selecting a minimal set of test cases from a large test suite, ensuring that the test cases selected cover a given set of requirements of a piece of software at the same time as it minimizes the amount of resources required for its execution. In this paper, we propose a Systolic Genetic Search (SGS) algorithm for solving the TSMP. SGS is a recently proposed optimization algorithm capable of taking advantage of the high degree of parallelism available in modern GPU architectures. The experimental evaluation conducted on a large number of test suites generated for seven real-world programs and seven large test suites generated for a case study from a real-world program shows that SGS is highly effective for the TSMP. SGS not only outperforms two competitive genetic algorithms, but also outperforms four heuristics specially conceived for this problem. The results also show that the GPU implementation of SGS has achieved a high performance, obtaining a large runtime reduction with respect to the CPU implementation for solutions with similar quality. The GPU implementation of SGS also shows an excellent scalability behavior when solving instances with a large number of test cases. As a consequence, the GPU-based SGS stands as a state of the art alternative for solving the TSMP in real-world software testing environments. (C) 2016 Elsevier B.V. All rights reserved.
Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an ap...
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Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions. (C) 2015 Elsevier B.V. All rights reserved.
In this paper numerical investigations are presented of how the axial position of the multiple reference frame (MRF) stator rotor interface between the inlet guide vanes (IGVs) and the impeller would influence the pre...
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ISBN:
(纸本)9780791850794
In this paper numerical investigations are presented of how the axial position of the multiple reference frame (MRF) stator rotor interface between the inlet guide vanes (IGVs) and the impeller would influence the predicted flow field for a turbocharger centrifugal compressor when simulated by the steady RANS method. In the first step, a total of three different axial positions of the MRF IGV-impeller interface were considered and compared with the results of an unsteady simulation to evaluate their accuracy. The results showed that the choice of the MRF interface location significantly influenced the predicted overall performance. At the lower rotational speed, the peak efficiency varied by 1.3% and the corresponding total pressure ratio varied by 0.022. At the high rotational speed, the different axial locations of the MRF interface varied the predicted choke point by 0.012 normalized mass flow rate. The mass flow rate of the near surge (NS) point was over estimated at both the high and low rotational speed by at least 0.038 normalized mass flow rate. Consideration of the flow field suggested that the MRF interface between the IGV and the impeller should be placed towards the upstream side of the available region to avoid being unphysically influenced by its interaction with the non-uniform pressure in the downstream subsonic flow field and to enable a more accurate prediction of the extent of the inducer shock in transonic operating situations. Based on this understanding, a further improvement was made for the setting of the MRF interface by employing a polyline interface. This achieved a more accurate numerical result for the NS operating point at low rotational speeds. The position of the MRF interface for modelling IGVs in a turbocharger compressor should be suitably chosen according to the objectives of the numerical study.
In this paper, the problem of obtaining optimal routes on tridimensional environments is discussed. This scenario is called as Traveler Salesman Problem (TSP 3D-variation). As is widely known, TSP has NP-complexity so...
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In this paper, the problem of obtaining optimal routes on tridimensional environments is discussed. This scenario is called as Traveler Salesman Problem (TSP 3D-variation). As is widely known, TSP has NP-complexity so is necessary to apply techniques to solve it approximately (no exacts solutions available). The purpose of this research is to present a genetic algorithm to solve 3D-TSP variation. These kind of evolutionary algorithms are ideal for solving complex problems where necessary rearrangements and route optimization. In case of genetic algorithms, optimal solutions appear faster depending on the quality of initial population, so theory recommends using metaheuristics for generating this population. In this study, it has used a metaheuristic GRASP algorithm to generate the initial population and, over it, apply the genetic operators proposed for optimizing individuals obtained. The results have optimal routes of movement and displacement and are directly applicable in the storage industry.
The output power of wind farms is fluctuating as a result of the wind speed variation. These power fluctuations disturb the quality of voltage and frequency of the grid. The fluctuated power can be smoothed via many m...
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The output power of wind farms is fluctuating as a result of the wind speed variation. These power fluctuations disturb the quality of voltage and frequency of the grid. The fluctuated power can be smoothed via many methods such as controlling the pitch angle of blades, the rotor inertia, and using the energy storage systems (ESSs). These ESSs are mostly installed in the renewable energy for load levelling with cost limitations. Owing to the high cost of ESSs, it is required to utilise and enhance different power control techniques. This study introduces different and recent proposed methods and enhancements to smoothen the output power of permanent-magnet synchronous generator driven directly (gearless) by a variable-speed wind turbine. Fuzzy logic control, sliding mode control, and evolutionary algorithms are used to enhance the control performance of pitch angle, machine-side converter, and DC–DC converter of ESS.
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