Hybrid particle swarm optimization with simulated annealing

Wang, Xihuai; Li, Junjun

doi:10.1109/icmlc.2004.1382205

Cited by 34 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The convergence rate of SA is very slow since it needs high annealing temperature. SAPSO can overcome the weak points of SA and PSO by learning from each other's strong points, which is the basic idea (Wang and Li, 2004).…”

Section: Simulated Annealing Based Particle Swarm Optimization Methodsmentioning

confidence: 99%

Study on the economic benefits of retired electric vehicle batteries participating in the electricity markets

Liu

et al. 2021

Journal of Cleaner Production

View full text Add to dashboard Cite

Section: Simulated Annealing Based Particle Swarm Optimization Methodsmentioning

confidence: 99%

Study on the economic benefits of retired electric vehicle batteries participating in the electricity markets

Liu

et al. 2021

Journal of Cleaner Production

View full text Add to dashboard Cite

“…w are the maximum and minimum inertia weight; t is the current number of iterations; T is the maximum number of iterations. The improved inertia weight can make the algorithm has a strong global search capability in the early stages of the evolution, but if the algorithm can not find the optimal point early in the search, with the decrease of the w, the local search capabilities will enhance and it is easy to fall into local minima [30,33]. This paper presents a dynamic change of the algorithm for the problems, it allows w to be dynamically adjusted as the fitness value of particle.…”

Section: B Fitness Functionmentioning

confidence: 99%

A Node Localization Algorithm for Wireless Sensor Networks based on Particle Swarm Algorithm

Chen¹,

Gong²,

Min³

2012

JNW

View full text Add to dashboard Cite

Node localization is the basis of a variety of applications as an important technology in wireless sensor networks. This paper studies the existing localization algorithms, and puts forward a sensor node localization algorithm based on particle swarm algorithm, this algorithm uses the fitness value of the particle to adjust the inertia weight value, in order to achieve , and fast and accurate positioning of the target node. The simulation results show that the algorithm is an effective refinement algorithm in nodes localization.

show abstract

“…Meanwhile, the SA method can quickly produce the local optimum value. Nevertheless, this method is considered to be weak in achieving the global optimum [20,21]. Therefore, a combination of GA and SA is expected to minimize the weaknesses and keep the advantages so that the combination method can produce the optimal solution in the shortest time possible.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Prediction of Drilling EMS-45 with Finite Element, Backpropagation Neural Network, and Metaheuristic Model

Effendi,

Pramono,

Suhardjono

et al. 2024

JMES Int. Journal of Mech. Eng. Sc.

View full text Add to dashboard Cite

Making holes with the minimum thrust force and torque using a drilling machine is challenging for researchers because of the difficulties in setting input parameters such as the type of drill tool, point of angle, and feeding speed. Therefore, the trial-and-error method to predict optimum input parameters through experiment can be replaced with the Back Propagation Neural Network (BPNN) and metaheuristic method (i.e., genetic algorithm (GA) and Simulated Annealing (SA)) method to reduce costs and time. BPNN can be used to represent the input-output correlation precisely. However, obtaining a model with minimum Mean Squared Error (MSE) requires much data for training, testing, and validation. Since the obtained data from experiments requires expensive costs, combining data from experimental and simulation using ANSYS should be considered to reduce the experimental costs. This study was then conducted to answer the research problem using an EMS 45 tool steel as the workpiece, with the three input parameters: type of drill tools (HSS M2 and HSS M35), the points of angle (118 and 134 degrees) and feeding speed rates (0.07 and 0.1 mm/s). The 32 data from experimental and modeling were used to model the correlation between the input and output parameters of the drilling process using BPNN. The BPNN's network model with minimum MSE is then used as the objective function to determine the input parameters to obtain the smallest value of thrust force and torque using the hybrid method using GA and SA. As the results, the optimum output parameter value in drilling for material EMS-45 (i.e., thrust force = 1615.2 N, and torque = 3.0236 Nm) can be obtained with the following input specifications: (a) type of drill tool = HSS-M2, (b) point of angle = 132.28 o , (c) feeding speed = 0.1 mm/s.

show abstract

Hybrid particle swarm optimization with simulated annealing

Cited by 34 publications

References 4 publications

Study on the economic benefits of retired electric vehicle batteries participating in the electricity markets

Study on the economic benefits of retired electric vehicle batteries participating in the electricity markets

A Node Localization Algorithm for Wireless Sensor Networks based on Particle Swarm Algorithm

Multi-Objective Prediction of Drilling EMS-45 with Finite Element, Backpropagation Neural Network, and Metaheuristic Model

Contact Info

Product

Resources

About