Due to the complexity of underlying data in a color image, retrieval of specific object features and relevant information becomes a complex task. Colour images have different color components and a variety of colour i...
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Due to the complexity of underlying data in a color image, retrieval of specific object features and relevant information becomes a complex task. Colour images have different color components and a variety of colour intensity which makes segmentation very challenging. In this paper we suggest a fitness function based on pixel-by-pixel values and optimize these values through evolutionary algorithms like differential evolution (DE), particle swarm optimization (PSO) and genetic algorithms (GA). The corresponding variants are termed GA-SA, PSO-SA and DE-SA;where SA stands for Segmentation Algorithm. Experimental results show that DE performed better in comparison of PSO and GA on the basis of computational time and quality of segmented image.
This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained op...
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This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained optimum design of a cantilever retaining wall. These algorithms are based on biological contests for survival and reproduction. The retaining wall optimization problem consists of two criteria, geotechnical stability and structural strength, while the final design minimizes an objective function. The objective function is defined in terms of both cost and weight. Constraints are applied using the penalty function method. The efficiency of the proposed method is examined by means of two numerical retaining wall design examples, one with a base shear key and one without a base shear key. The final designs are compared to the ones determined by genetic algorithms as classical metaheuristic optimization methods. The design results and convergence rate of the BBO algorithm show a significantly better performance than the other algorithms in both design cases.
evolutionary algorithms, EA's, try to imitate, in some way, the principles of natural evolution and genetics. They evolve a population of potential solutions to the problem using operators such as mutation, crosso...
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evolutionary algorithms, EA's, try to imitate, in some way, the principles of natural evolution and genetics. They evolve a population of potential solutions to the problem using operators such as mutation, crossover and selection. In general, the mutation operator is responsible for the diversity of the population and helps to avoid the problem of premature convergence to local optima (a premature stagnation of the search caused by the lack of population diversity). In this paper we present a new mutation operator in the context of Multi-Objective evolutionary algorithms, MOEA's, which makes use of the definition of Pareto optimality and manages the maximal amplitude or maximal step size of the mutation according to the Pareto layer of the individual and also of the iteration number. The behaviour of our mutation operator reveals that the use of variation operators which take into consideration the quality of the solutions, in terms of Pareto dominance or Pareto layers, can help to improve them. The Pareto based mutation operator proposed is compared with four well established and extensively used mutation operators: random mutation, non-uniform mutation, polynomial mutation and Gaussian mutation. The accomplished experiments reveal that our mutation operator performs, in most of the test problems considered, better than the others.
evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popul...
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evolutionary algorithms (EAs) are fast and robust computation methods for global optimization, and have been widely used in many real-world applications. We first conceptually discuss the equivalences of various popular EAs including genetic algorithm (GA), biogeography-based optimization (BBO), differential evolution (DE), evolution strategy (ES) and particle swarm optimization (PSO). We find that the basic versions of BBO, DE, ES and PSO are equal to the GA with global uniform recombination (GA/GUR) under certain conditions. Then we discuss their differences based on biological motivations and implementation details, and point out that their distinctions enhance the diversity of EA research and applications. To further study the characteristics of various EAs, we compare the basic versions and advanced versions of GA, BBO, DE, ES and PSO to explore their optimization ability on a set of real-world continuous optimization problems. Empirical results show that among the basic versions of the algorithms, BBO performs best on the benchmarks that we studied. Among the advanced versions of the algorithms, DE and ES perform best on the benchmarks that we studied. However, our main conclusion is that the conceptual equivalence of the algorithms is supported by the fact that algorithmic modifications result in very different performance levels. (C) 2013 Elsevier Ltd. All fights reserved.
From the viewpoint of "FUBEN-EKI," we have developed an inconvenient input method for mobile devices, which provides us with chances of exploration. In the proposed mobile device, users provide a large numbe...
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Many chaotic dynamical systems can produce time series with a wide range of temporal and spectral properties as a function of only a few fixed parameters. This malleability invites their use as tools for shaping or de...
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Many chaotic dynamical systems can produce time series with a wide range of temporal and spectral properties as a function of only a few fixed parameters. This malleability invites their use as tools for shaping or designing inputs used to drive a separate dynamical system of interest. By specifying an objective function and employing an evolutionary algorithm to manipulate the parameters governing the dynamics of the forcing system, the output of the driven system is made to approach an optimal response subject to desired constraints. The technique's versatility is demonstrated for two different applications: damage detection in structures and phase-locked loop disruption.
In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft'...
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In this paper, an Aircraft Research Flight Simulator equipped with Flight Dynamics Level D (highest level) was used to collect flight test data and develop new controller methodologies. The changes in the aircraft's mass and center of gravity position are affected by the fuel burn, leading to uncertainties in the aircraft dynamics. A robust controller was designed and optimized using the H-infinity method and two different metaheuristic algorithms;in order to ensure acceptable flying qualities within the specified flight envelope despite the presence of uncertainties. The H 1 weighting functions were optimized by using both the genetic algorithm, and the differential evolution algorithm. The differential evolution algorithm revealed high efficiency and gave excellent results in a short time with respect to the genetic algorithm. Good dynamic characteristics for the longitudinal and lateral stability control augmentation systems with a good level of flying qualities were achieved. The optimal controller was used on the Cessna Citation X aircraft linear model for several flight conditions that covered the whole aircraft's flight envelope. The novelty of the new objective function used in this research is that it combined both time-domain performance criteria and frequency-domain robustness criterion, which led to good level aircraft flying qualities specifications. The use of this new objective function helps to reduce considerably the calculation time of both algorithms, and avoided the use of other computationally more complicated methods. The same fitness function was used in both evolutionary algorithms (differential evolution and genetic algorithm), then their results for the validation of the linear model in the flight points were compared. Finally, robustness analysis was performed to the nonlinear model by varying mass and gravity center position. New tools were developed to validate the results obtained for both linear and nonlinear aircraft models. It was co
Some problems related to evolutionary and genetic algorithms, genetic programming, and neural-network computations on solving applied problems that are reduced to analysis of functions prescribed at permutations are r...
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Some problems related to evolutionary and genetic algorithms, genetic programming, and neural-network computations on solving applied problems that are reduced to analysis of functions prescribed at permutations are roughly studied. Natural parallelism of these algorithms and possibility of their realization on modern computers are noted.
evolutionary algorithms are randomized search heuristics that were invented in the sixties and have been intensively applied and studied since the eighties. Since then there have been only a few theoretical investigat...
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evolutionary algorithms are randomized search heuristics that were invented in the sixties and have been intensively applied and studied since the eighties. Since then there have been only a few theoretical investigations and no sound theoretical foundation. One of the main sources of difficulty for theoretical analyses is the crossover operator. It can be useful only if the current population of strings, has a certain diversity. Here it is proved that an evolutionary algorithm can produce enough diversity such that the use of crossover can speedup the expected optimization time from superpolynomial to a polynomial of small degree.
The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, wh...
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The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward-backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.
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