Hybrid optimization algorithm based on chaos, cloud and particle swarm optimization algorithm

2013 ◽  
Vol 24 (2) ◽  
pp. 324-334 ◽  
Author(s):  
Mingwei Li ◽  
Haigui Kang ◽  
Pengfei Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Optimization ◽  
Swarm Optimization ◽  
Hybrid Optimization Algorithm

Hybrid Optimization Approach for Gas-Assisted Injection Molding Based on Metamodeling and Particle Swarm Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3353-3356
Author(s):  
Wei Chen ◽  
Xian Hong Han ◽  
Xiong Hui Zhou ◽  
Xue Wei Ge
Keyword(s):  
Particle Swarm Optimization ◽  
Injection Molding ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Optimization ◽  
Particle Swarm Algorithm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Plastic Process

As a new plastic process technique, Gas-assisted injection molding has many advantages comparing to the traditional injection molding. Meanwhile, Optimization of Gas-assisted injection molding is more complex since many additional parameters have been introduced to the process. In this paper, a hybrid optimization approach based on metamodeling and particle swarm optimization algorithm is proposed and applied for Gas-assisted injection molding. Moreover, the validation of the approach will be illustrated through the optimization process of a real panel.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
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.

scholarly journals Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

2021 ◽  
Author(s):  
Noel Jose Thengappurackal Laiju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.

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

2019 ◽  
Vol 22 (15) ◽  
pp. 3262-3276 ◽  
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.

scholarly journals Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

2021 ◽  
Author(s):  
Noel Jose Thengappurackal Laiju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.

Sign in / Sign up

Export Citation Format

Share Document