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A Novel Method for Image Segmentation Based on Simplified Pulse Couple Neural Network and Gbest Led gravitational search algorithm

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IEEE ACCESS 2019年 7卷 21310-21330页

作者： Jiao, Keming Pan, Zhongliang South China Normal Univ Sch Phys & Telecommun Engn Guangzhou 510000 Guangdong Peoples R China

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.

关键词： Image segmentation gravitational search algorithm pulse coupled neural network edge matching noise control

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An enhanced quantum-inspired gravitational search algorithm for color prediction based on the absorption spectrum

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TEXTILE REsearch JOURNAL 2021年第11-12期91卷 1211-1226页

作者： Gao, Zehai Zhang, Yan Zhou, Shisheng Lyu, Wei Xian Univ Technol Jinhua South Rd 5 Xian 710048 Shaanxi Peoples R China Shenzhen Jinjia Grp Co Ltd Shenzhen Peoples R China

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 search algorithm 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 search algorithm 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.

关键词： color prediction gravitational search algorithm absorption spectrum color difference

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A Modified gravitational search algorithm for Function Optimization

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IEEE ACCESS 2019年 7卷 5984-5993页

作者： He, Shoushuai Zhu, Lei Wang, Lei Yu, Lu Yao, Changhua Army Engn Univ PLA Coll Commun Engn Nanjing 210007 Jiangsu Peoples R China

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.

关键词： gravitational search algorithm repulsive force exponential Kbest function optimization

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A HYBRID GENETIC algorithm AND gravitational search algorithm FOR GLOBAL OPTIMIZATION

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NEURAL NETWORK WORLD 2015年第1期25卷 53-73页

作者： Zhang, Aizhu Sun, Genyun Wang, Zhenjie Yao, Yanjuan China Univ Petr Sch Geosci Qingdao 266580 Shandong Peoples R China Minist Environm Protect MEP China SEC Beijing 100094 Peoples R China

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.

关键词： Heuristic algorithms genetic algorithm gravitational search algorithm optimization

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Efficient gravitational search algorithm for optimum design of retaining walls

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STRUCTURAL ENGINEERING AND MECHANICS 2013年第1期45卷 111-127页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan Eslami, Mahdiyeh Natl Univ Malaysia Dept Civil & Struct Engn Bangi Selangor Malaysia Islamic Azad Univ Dept Elect Engn Sci & Res Branch Kerman Iran

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.

关键词： retaining wall minimum weight minimum cost minimum CO2 emissions gravitational search algorithm

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A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams.

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APPLIED SOFT COMPUTING 2014年第0期16卷 223-233页

作者： Khatibinia, M. Khosravi, Sh. Univ Birjand Dept Civil Engn Birjand Iran Islamic Azad Univ Kerman Branch Young Researchers & Elite Club Kerman Iran

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.

关键词： gravitational search algorithm Orthogonal crossover Concrete gravity dams Optimal shape Weighted least squares support vector machine

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An improved gravitational search algorithm for global optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 5039-5047页

作者： Yu Xiaobing Yu Xianrui Chen Hong Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Jiangsu Peoples R China

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.

关键词： Heuristic optimization algorithm gravitational search algorithm gravitational coefficient global optimization

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A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第Feb期55卷 628-644页

作者： Jiang, Shanhe Ji, Zhicheng Shen, Yanxia Jiangnan Univ Inst Elect Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Anqing Normal Coll Dept Phys & Power Engn Anqing 246011 Peoples R China

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.

关键词： Particle swarm optimization gravitational search algorithm Hybrid evolutionary approach Benchmark test functions Economic emission load dispatch Practical constraints

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A novel approach for economic dispatch of hydrothermal system via gravitational search algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2014年 247卷 535-546页

作者： Yuan, Xiaohui Ji, Bin Chen, Zhijun Chen, Zhihuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil Engn & Mech Wuhan 430074 Peoples R China

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.

关键词： Cascaded reservoirs Economic dispatching gravitational search algorithm Heuristic search strategy

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Self-Adaptive gravitational search algorithm With a Modified Chaotic Local search

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IEEE ACCESS 2017年 5卷 17881-17895页

作者： Ji, Junkai Gao, Shangce Wang, Shuaiqun Tang, Yajiao Yu, Hang Todo, Yuki Univ Toyama Fac Engn Toyama 9308555 Japan Shanghai Maritime Univ Informat Engn Coll Shanghai 201306 Peoples R China Cent South Univ Forestry & Technol Sch Econ Changsha 410014 Hunan Peoples R China Taizhou Univ Coll Comp Sci & Technol Taizhou 225300 Peoples R China Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

The gravitational search algorithm (GSA) has been proved to yield good performance in solving various optimization problems. However, it is inevitable to suffer from slow exploitation when solving complex problems. In this paper, a thorough empirical analysis of the GSA is performed, which elaborates the role of the gravitational parameter G in the optimization process of the GSA. The convergence speed and solution quality are found to be highly sensitive to the value of G. A self-adaptive mechanism is proposed to adjust the value of G automatically, aiming to maintain the balance of exploration and exploitation. To further improve the convergence speed of GSA, we also modify the classic chaotic local search and insert it into the optimization process of the GSA. Through these two techniques, the main weakness of GSA has been overcome effectively, and the obtained results of 23 benchmark functions confirm the excellent performance of the proposed method.

关键词： gravitational search algorithm optimization self-adaptive chaotic exploration and exploitation

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