Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color ...
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Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation searchalgorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation searchalgorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.
gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton's laws of gravity and motion. Although GSA provides a good performance in solving optimization problems, ...
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gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton's laws of gravity and motion. Although GSA provides a good performance in solving optimization problems, it has a disadvantage of premature convergence. In this paper, the concept of repulsive force is introduced and the definition of exponential Kbest is used in a new version of GSA, which is called repulsive GSA with exponential Kbest (EKRGSA). In this algorithm, heavy particles repulse or attract all particles according to distance, and all particles search the solution space under the combined action of repulsive force and gravitational force. In this way, the exploration ability of the algorithm is improved and a proper balance between exploration and exploitation is established. Moreover, the exponential Kbest significantly decreases the computational time. The proposed algorithm is tested on a set of benchmark functions and compared with other algorithms. The experimental results confirm the high efficiency of EKRGSA.
The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which finds optimal regions of complex search spaces through the interaction of individuals in a population of particles. Alt...
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The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which finds optimal regions of complex search spaces through the interaction of individuals in a population of particles. Although GSA has proven effective in both science and engineering, it is still easy to suffer from premature convergence especially facing complex problems. In this paper, we proposed a new hybrid algorithm by integrating genetic algorithm (GA) and GSA (GA-GSA) to avoid premature convergence and to improve the search ability of GSA. In GA-GSA, crossover and mutation operators are introduced from GA to GSA for jumping out of the local optima. To demonstrate the search ability of the proposed GA-GSA, 23 complex benchmark test functions were employed, including unimodal and multimodal high-dimensional test functions as well as multimodal test functions with fixed dimensions. Wilcoxon signed-rank tests were also utilized to execute statistical analysis of the results obtained by PSO, GSA, and GA-GSA. Experimental results demonstrated that the proposed algorithm is both efficient and effective.
This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational searc...
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This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational search algorithm (GSA) with gbest agent memory ability. To evaluate the performance of GLGSA, we applied it to 23 standard benchmark functions and compared with GSA and GGSA. The results showed that the GLGSA had better performance in term of convergence and avoidance of local minima. Besides, in order to improve the accuracy of segmentation, the fitness function consisted of cross entropy parameter, edge matching, and noise control. To verify the efficiency of our method, we compared it with the state-of-the-art algorithms, such as Otsu, GA Renyi, and PSO-PCNN, using the gray nature images from the Berkeley segmentation dataset. Finally, the subjective visual analysis and quantitative analysis that included the uniformity measure, region contrast measure, structural similarity, and comprehensive evaluation were used to evaluate the segmented images. The comparison results demonstrated that our proposed method could get better segmentation results.
In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs ...
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In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and CO2 emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.
A hybrid approach based on an improved gravitational search algorithm (IGSA) and orthogonal crossover (OC) is proposed to efficiently find the optimal shape of concrete gravity dams. The proposed hybrid approach is ca...
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A hybrid approach based on an improved gravitational search algorithm (IGSA) and orthogonal crossover (OC) is proposed to efficiently find the optimal shape of concrete gravity dams. The proposed hybrid approach is called IGSA-OC. The hybrid of IGSA and the OC operator can improve the global exploration ability of the IGSA method, and increase its convergence rate. To find the optimal shape of concrete gravity dams, the interaction effects of dam-water-foundation rock subjected to earthquake loading are considered in this study. The computational cost of the optimal shape of concrete gravity dams subjected earthquake loads is usually high. Due to this problem, the weighted least squares support vector machine (WLS-SVM) regression as an efficient metamodel is utilized to considerably predict dynamic responses of gravity dams by spending low computational cost. To testify the robustness and efficiency of the proposed IGSA-OC, first, four well-known benchmark functions in literatures are optimized using the proposed IGSA-OC, and provides comparisons with the standard gravitational search algorithm (GSA) and the other modified GSA methods. Then, the optimal shape of concrete gravity dams is found using IGSA-OC. The solutions obtained by the IGSA-OC are compared with those of the standard GSA, IGSA and particle swarm optimization (PSO). The numerical results demonstrate that the proposed IGSA-OC significantly outperforms the standard GSA, IGSA and PSO. (C) 2013 Elsevier B.V. All rights reserved.
gravitational search algorithm (GSA) is inspired by swarm behaviors in nature and physical law based on Newtonian gravity and the laws of motion. There are two key parameters including the number of applied agents (Kb...
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gravitational search algorithm (GSA) is inspired by swarm behaviors in nature and physical law based on Newtonian gravity and the laws of motion. There are two key parameters including the number of applied agents (Kbest) and gravitational coefficient (G) to control the search progress in the algorithm. In the conventional GSA, the acceleration of the agents is mainly determined by Kbest and G. Kbest and G are calculated by a monotonically decreasing function, which is not a good schedule for solving complex problems. In order to solve the problem and accelerate the convergence of algorithm, an adaptive GSA is proposed, in which Kbest and G calculation method for strengthening exploitation capability are implemented to achieve better optimization results. Extensive experimental results based on benchmark functions are provided to show the effectiveness of the proposed method. The obtained results have been compared with the results of the original GSA, CGSA, and CLPSO. The comparison results have revealed that the proposed method has good performances.
Particle swarm optimization (PSO) is inspired by social behaviors of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses, and both of them are pertain to m...
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Particle swarm optimization (PSO) is inspired by social behaviors of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses, and both of them are pertain to meta-heuristic algorithms. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO-GSA), having attributes of PSO and GSA, is proposed in this paper to solve economic emission load dispatch (EELD) problems considering various practical constraints. These constraints consist of the generator ramp rate limits, non-convex and discontinuous nature of prohibited operating zones, non-smooth characteristic of valve-point effects, multiple fuels type of generation units, and transmission losses in realistic power systems. The proposed approach embodies interesting concepts and fully incorporates the social essence of PSO with the motion mechanism of GSA. The proposed HPSO-GSA adopts co-evolutionary technique to simultaneously update particle positions with PSO velocity and GSA acceleration. HPSO-GSA, therefore, is expected to obtain an efficient balance between exploration and exploitation. From results of canonical benchmark test functions, HPSO-GSA does significantly improve PSO and GSA with better performance. As a real application, the EELD problems on five test systems including different constraints are solved by the HPSO-GSA to assess the optimizing performance of the proposed hybrid approach. The results obtained confirm the potential and effectiveness of the proposed approach compared to PSO, GSA and other algorithms published in the recent state-of-the art literatures for the solution of the EELD problems. (C) 2013 Elsevier Ltd. All rights reserved.
The daily economic dispatching of hydrothermal system (DHS), which is a large-scale dynamic nonlinear constrained optimization problem, plays an important role in economic operation of electric power systems. This pap...
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The daily economic dispatching of hydrothermal system (DHS), which is a large-scale dynamic nonlinear constrained optimization problem, plays an important role in economic operation of electric power systems. This paper proposes a novel enhanced gravitational search algorithm (EGSA) to solve DHS problem. In the proposed method, the improvements mainly include three aspects. Firstly, particle swarm optimization (PSO) that acts complementary is integrated into gravitational search algorithm for update of agent's velocity. Secondly, heuristic search strategies based random selected dependent discharge of hydro plants and average full-load cost priority list of thermal units are adopted to deal with equality constraints of DHS problem. Thirdly, feasibility-based selection comparison techniques are devised to effectively handle inequality constraints in EGSA, which do not require penalty factors or extra parameters and can guide the agent to the feasible region quickly. The feasibility and effectiveness of the proposed EGSA method is verified by a hydrothermal test system and the simulation results are compared with those of differential evolution, PSO, genetic algorithm, classical evolutionary programming, fast evolutionary programming, and improved fast evolutionary programming algorithm. From the results, it clearly shows that the proposed method gives better quality solutions than other methods. (C) 2014 Elsevier Inc. All rights reserved.
Path planning of Uninhabited Aerial Vehicle(UAV) is a complicated global optimum *** the paper,an improved gravitational search algorithm(GSA) was proposed to solve the path planning *** searchalgorithm(GSA) is a new...
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Path planning of Uninhabited Aerial Vehicle(UAV) is a complicated global optimum *** the paper,an improved gravitational search algorithm(GSA) was proposed to solve the path planning *** searchalgorithm(GSA) is a newly presented under the inspiration of the Newtonian gravity,and it is easy to fall local *** the basis of introducing the idea of memory and social information of Particle Swarm Optimization(PSO),a novel moving strategy in the searching space was designed,which can improve the quality of the optimal ***,a weighted value was assigned to inertia mass of every agent in each iteration process to accelerate the convergence speed of the *** position was updated according to the selection rules of survival of the *** this way,the population is always moving in the direction of the optimal *** feasibility and effectiveness of our improved GSA approach was verified by comparative experimental results with PSO,basic GSA and two other GSA models.
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