Particle Swarm Optimization Guided Genetic Algorithm: A Novel Hybrid Optimization Algorithm

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

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Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

Discrete Dynamics in Nature and Society ◽

10.1155/2016/1516271 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Jianwen Guo ◽

Zhenzhong Sun ◽

Hong Tang ◽

Xuejun Jia ◽

Song Wang ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Preventive Maintenance ◽

Particle Swarm ◽

Cuckoo Search ◽

Hybrid Optimization ◽

Normal Operation ◽

Maintenance Period ◽

Swarm Optimization ◽

Hybrid Optimization Algorithm

All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

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Damage identification of bridge structure considering temperature variations based on particle swarm optimization - cuckoo search algorithm

Advances in Structural Engineering ◽

10.1177/1369433219861728 ◽

2019 ◽

Vol 22 (15) ◽

pp. 3262-3276 ◽

Cited By ~ 7

Author(s):

Minshui Huang ◽

Yongzhi Lei ◽

Shaoxi Cheng

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Damage Identification ◽

Particle Swarm ◽

Optimal Solution ◽

Cuckoo Search ◽

Hybrid Optimization ◽

Swarm Optimization ◽

Temperature Variations ◽

Hybrid Optimization Algorithm

Structures are always exposed to environmental conditions such as varying temperatures and noises; as a consequence, the dynamic features of structures are changed accordingly. But the model-based methods, used to detect damage using optimization algorithms to get global optimal solution, are highly sensitive to environmental conditions, experimental noises, or numerical errors. While the mechanisms of optimization algorithms are limited by local optimal solution, their convergences are not always assured. In the study, a model-based damage-identification method considering temperature variations, comprised of particle swarm optimization and cuckoo search, is implemented to detect structural damage. First, to eliminate the influence of environmental temperature, temperature change is considered as a parameter of structural material elastic modulus. A function relationship is established between environmental temperature and the material elastic modulus, and an objective function composed of natural frequency, mode shape and modal strain energy with different weight coefficients is constructed. Second, the hybrid optimization algorithm, a combination of particle swarm optimization and cuckoo search, is proposed. Third, to solve the problem of optimization algorithm convergence, the optimization performance of the hybrid optimization algorithm is validated by utilizing four benchmark functions, and it is found that the performance of the hybrid optimization algorithm is the best. In order to test the performance of the three algorithms in damage identification, a numerical simply supported beam is adopted. The results show that the hybrid optimization algorithm can identify the damage location and severity under four different damage cases without considering temperature variations and two cases considering temperature variations. Finally, the hybrid optimization algorithm is introduced to test the damage-identification performance of I-40 Bridge, an actual steel–concrete composite bridge under temperature variations, whose results show that the hybrid optimization algorithm can preferably distinguish between real damages and temperature effects (temperature gradient included); its good robustness and engineering applicability are validated.

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Hybrid Optimization Algorithm of Improved Binary Particle Swarm Optimization (iBPSO) And Cuckoo Search for Review Spam Detection

Proceedings of the 9th International Conference on Machine Learning and Computing - ICMLC 2017 ◽

10.1145/3055635.3056647 ◽

2017 ◽

Cited By ~ 3

Author(s):

S. P. Rajamohana ◽

K. Umamaheswari

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm ◽

Cuckoo Search ◽

Hybrid Optimization ◽

Spam Detection ◽

Binary Particle Swarm Optimization ◽

Swarm Optimization ◽

Hybrid Optimization Algorithm ◽

Review Spam

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An anycast routing algorithm based on the combination of particle swarm optimization algorithm and genetic algorithm

Management Innovation and Information Technology ◽

10.2495/miit130641 ◽

2014 ◽

Author(s):

Taoshen Li ◽

Zhihui Ge ◽

Qin Xiong

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Routing Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Anycast Routing

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Particle Swarm Optimization Algorithm vs. Genetic Algorithm to Solve Multi-Objective Optimization Problem in Gait Planning of Biped Robot

Advances in Intelligent and Soft Computing - Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012 ◽

10.1007/978-3-642-27443-5_65 ◽

2012 ◽

pp. 563-570 ◽

Cited By ~ 1

Author(s):

Rega Rajendra ◽

Dilip Kumar Pratihar

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Optimization Problem ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Biped Robot ◽

Multi Objective Optimization ◽

Swarm Optimization ◽

Gait Planning

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Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽

10.3390/en13112873 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2873 ◽

Cited By ~ 1

Author(s):

Dinh Thanh Viet ◽

Vo Van Phuong ◽

Minh Quan Duong ◽

Quoc Tuan Tran

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Wind Power ◽

Optimization Algorithm ◽

Electricity Market ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Short Term ◽

Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

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