Proposed is a new robust image registration method that can perform subpixel image registration for different-exposed images with highly photometric distortion. In this method, phase congruency is used to eliminate th...
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Proposed is a new robust image registration method that can perform subpixel image registration for different-exposed images with highly photometric distortion. In this method, phase congruency is used to eliminate the effects of illumination change and photometric distortion for image registration using the phase correlation technique. Considering the presence of non-overlapped regions owing to photometric distortion, evolutionary programming is applied to search for the accurate translation parameters. Experimental results show that the proposed method is efficient and robust against illumination change and photometric distortion.
An evolutionary programming algorithm with a mixed continuous-discrete parameter representation for application in electromagnetic optimization problems is presented. In our approach, the mutation operator consists of...
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An evolutionary programming algorithm with a mixed continuous-discrete parameter representation for application in electromagnetic optimization problems is presented. In our approach, the mutation operator consists of a hybrid combination of Gaussian mutation for the continuous parameters, and Poisson mutation for the discrete parameters. The implementation uses self-adaptive schemes for updating the standard deviation of the Gaussian distribution and the mean of the Poisson distribution during the evolution. As an example, the proposed evolutionary algorithm is applied to the constraint designs of various multilayer dielectric-filter structures. (C) 2003 Wiley Periodicals, Inc.
'Three-link-model' Exergoeconotnic methodology optimises the design and operability of a system. Contrary to traditional iterative Exergoeconomic optimisation methods, a reversed method is used, since assumpti...
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'Three-link-model' Exergoeconotnic methodology optimises the design and operability of a system. Contrary to traditional iterative Exergoeconomic optimisation methods, a reversed method is used, since assumptions considered by Tsatsaronis for calculating cost-optimal Exergetic efficiency and relative cost difference, were not applicable, new assumptions have been adopted. In a case study applied to Mazandaran paper industry, iterative reversed optimisation results have been compared with evolutionary programming results. Replacement of Pressure Valve and Direct Cyclone Contact Evaporation is proposed, while by selection of the optimum decision variable, recoverable black liquor is increased by 7% and energy consumption is decreased by 12%.
This paper presents a modified hybrid evolutionary programming-sequential quadratic programming (MHEP-SQP) method to solve the dynamic economic dispatch problem (DEDP) of generating units considering the valve-point e...
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This paper presents a modified hybrid evolutionary programming-sequential quadratic programming (MHEP-SQP) method to solve the dynamic economic dispatch problem (DEDP) of generating units considering the valve-point effects. The proposed method is a two-phase optimizer. In the first phase, the candidates of EP will explore the solution space freely. In the second phase, the SQP will be invoked when there is an improvement of solution (a feasible solution) in the EP run. Thus, the SQP guides EP for better performance in the complex solution space. To validate the effectiveness of the proposed method, a 10-unit system is studied under three different cases. Feasibility of the MHEP-SQP for the DEDP over EP and EP-SQP methods is shown in general. (c) 2005 Elsevier Ltd. All rights reserved.
Product configuration immensely influences the suitability of a product for end-of-life (ECL) disassembly. The product configuration is the relative spatial and logical arrangement of the different parts/sub-assemblie...
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Product configuration immensely influences the suitability of a product for end-of-life (ECL) disassembly. The product configuration is the relative spatial and logical arrangement of the different parts/sub-assemblies of the product with respect to each other. The complexity involved in studying the influence of configuration design on EOL disassembly has limited the scope of the current design for disassembly (DfD) approaches to guideline-based prescriptive methods and index-based evaluation techniques. The application of these approaches has primarily been limited to specific case studies of product redesign. Many of the current methods do not provide the necessary rigor that will lead to the creation of a theoretical base for addressing product configuration issues which is indispensable during product redesign. Though fraught with obstacles, studying the effects of product configuration on DfD will be useful to develop automated configuration optimization methods for EOL disassembly. To this end, a model to study the combinatorial configuration design optimization problem from a disassembly perspective is described in this study. The different structural principles of the design space derived in this study provide insights into the possibilities and the natural shortcomings of automated optimization of a product by relating the effects of design constraints and disassembly requirements on product redesign. A hierarchical evolutionary programming based algorithm is also developed to test the design solutions generated by the proposed model. (c) 2005 Elsevier Ltd. All rights reserved.
This paper addresses a solution of simultaneous localization and mapping ( SLAM) for sonar readings based on neuro-evolutionary optimization algorithm. In the past two decades, numerous studies have attempted to solve...
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This paper addresses a solution of simultaneous localization and mapping ( SLAM) for sonar readings based on neuro-evolutionary optimization algorithm. In the past two decades, numerous studies have attempted to solve the SLAM problem using laser scanners and vision sensors. However, relatively little research has been carried out on a sonar-based SLAM algorithm, because the bearing accuracy and resolution of sonars are not enough to find consistent features for SLAM. The proposed algorithm in this paper solves the sonar-based SLAM as a global optimization problem using the cost function that represents the quality of a robot's trajectory in the world coordinate frame. In our algorithm, a neural network helps to estimate the robot's pose error accurately using sonar inputs at each position and the pose difference between two consecutive robot poses, and evolutionary programming is used to find the most suitable neural network. By way of learning and evolution, our algorithm does not need a prior assumption on the motion and sensor models, and therefore shows a robust performance regardless of the actual noise type. Our neural network-based SLAM algorithm is applied to a robot that has sonar sensors. The various experimental results demonstrate that the neural network-based SLAM guarantees a consistent environmental map under sonar readings that in general are known to have poor bearing accuracy and resolution. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2010
This paper proposes a variable-dimension optimization approach to address the high dimensionality issues in solving the unit commitment problem. This method introduces the concept of adaptive search space dimension. T...
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This paper proposes a variable-dimension optimization approach to address the high dimensionality issues in solving the unit commitment problem. This method introduces the concept of adaptive search space dimension. The proposed approach is implemented in particle swarm optimization algorithm. The optimization process starts with an arbitrary problem dimension, adapts with respect to the swarm progress and finally selects the optimal dimensional space. The efficiency of this method is tested on a ten-unit test system. The results are compared with binary programming and fixed duty cycle approaches. The simulation results show that the proposed method results in considerable reduction of problem dimension, faster convergence and improved quality of the final solution.
This article presents an attempt to explore the feasibility of an improved fast evolutionary program (IFEP) search technique to solve extremely challenging nonconvex economic load dispatch (ELD) problem with transmiss...
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This article presents an attempt to explore the feasibility of an improved fast evolutionary program (IFEP) search technique to solve extremely challenging nonconvex economic load dispatch (ELD) problem with transmission losses involving variations of consumer load patterns. The effectiveness of the proposed approach has been tested successfully on the standard 6-bus system, IEEE 14-bus system, and the IEEE-30 bus system with several heuristic load patterns. The numerical results reveal that the proposed approach can provide better optimal dispatch solutions than those of classical lambda-iteration method (CLIM). Aside from this, the computation time is reasonable even with nonconvex fuel cost functions where the gradient based search methods are inapplicable.
A weighted switching strategy and an inner-loop compensator are presented in this paper to design an observer-based tracker for a decentralized closed-loop cascaded system with a saturating actuator and state constrai...
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A weighted switching strategy and an inner-loop compensator are presented in this paper to design an observer-based tracker for a decentralized closed-loop cascaded system with a saturating actuator and state constraints. The LQR design methodology for the observer-based tracker is proposed to simplify the complexity of the decentralized control. The realizable sample-data controller with a low-gain property and a high design performance is realized through the digital redesign method. For obtaining a better design performance, evolutionary programming is then presented to tune the parameters of the tracker. Some examples are also presented to demonstrate the effectiveness of the proposed methodology. (c) 2007 Elsevier Ltd. All rights reserved.
There has been renewed interest in using simulated evolution to address difficult optimization problems. These simulations can be divided into two groups: (1) those that model chromosomes and emphasize genetic operato...
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There has been renewed interest in using simulated evolution to address difficult optimization problems. These simulations can be divided into two groups: (1) those that model chromosomes and emphasize genetic operators; and (2) those that model individuals or populations and emphasize the adaptation and diversity of behavior. Recent claims have suggested that genetic models using recombination operators, specifically crossover, are typically more efficient and effective at function optimization than behavioral models that rely solely on mutation. These claims are assessed empirically on a broad range of response surfaces.
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