Fast Drilling Optimizer for Drilling Automation

2021 ◽  
Author(s):  
John Abish Giftson Joy ◽  
Robello Samuel
Keyword(s):  
Real Time ◽  
Field Data ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Solution Space ◽  
Time Data ◽  
Swarm Optimization ◽  
The Given ◽  
Drilling Time

Abstract The rate of penetration (ROP) was optimized using a particle swarm optimization algorithm for real-time field data to reduce drilling time and increase efficiency. ROP is directly related to drilling costs and is a major factor in determining mechanical specific energy, which is often used to quantify drilling efficiency. Optimization of ROP can therefore help cut down costs associated with drilling. ROP values were chosen from real-time field data, accounting for weight on bit, bit rotation, flow rate variation along with bit wear. A random forest regressor was used to find correlations between the dependent parameters. The parameters were then optimized for the given constraints to find the optimal solution space. The boundary constraints for the ROP function were determined from the real-time data. The function parameters were optimized using a particle swarm optimization algorithm. This is a meta-heuristic model used to optimize an objective function for its maximum or minimum within given constraints. The optimization method makes use of a population of solution particles which act as the particle swarm. These particles move collectively in the given solution space controlled by a mathematical model based on their position and velocity. This model makes use of the best-known solution for each particle and the global best position of the system to guide the swarm towards the optimal solution. The function was optimized for each well, providing optimal ROP values during real-time drilling. A fast drilling optimizer is crucial to automate and streamline the drilling process. This simultaneous optimization of ROP based on real-time data can be implemented during the process thereby increasing the efficiency of drilling as well as reducing the required drilling time.

scholarly journals Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem

2016 ◽  
Vol 11 (1) ◽  
pp. 58-67 ◽  
Author(s):  
S Sarathambekai ◽  
K Umamaheswari
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Initial Population ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

2013 ◽  
Vol 475-476 ◽  
pp. 956-959 ◽  
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

scholarly journals Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

2015 ◽  
Vol 1 (3) ◽  
pp. 125
Author(s):  
Kanagasabai Lenin ◽  
Bhumanapally Ravindhranath Reddy ◽  
Munagala Surya Kalavathi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Position Vector ◽  
Swarm Optimization ◽  
Power Problem

In this paper a Progressive particle swarm optimization algorithm (PPS) is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits.

scholarly journals Research on Microgrid Optimization Based on Simulated Annealing Particle Swarm Optimization

2019 ◽  
Vol 118 ◽  
pp. 01038
Author(s):  
Shuyi Li ◽  
Xifeng Zhou ◽  
Qiangang Guo
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Storage Device ◽  
Swarm Optimization

Based on the pursuit of different goals in the operation of the microgrid, it is not possible to meet the lowest cost and the least pollution at the same time. From the perspective of economy and environmental protection, a microgrid model including photovoltaic power generation, wind power generation, micro gas turbine, fuel cell and energy storage device is proposed. This paper establishes a comprehensive benefit objective function that considers both microgrid fuel cost, maintenance management cost, depreciation cost, interaction cost with public grid and pollutant treatment cost. In order to avoid the defect that the traditional particle swarm optimization algorithm is easy to fall into the local optimal solution, this paper uses the combination of simulated annealing algorithm and particle swarm optimization algorithm to compare with the traditional particle swarm optimization algorithm to obtain a more suitable method for microgrid operation. Finally, a typical microgrid in China is taken as an example to verify the feasibility of the algorithm.

scholarly journals An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm

2018 ◽  
Vol 36 (4) ◽  
pp. 722-727
Author(s):  
Guoqing Shi ◽  
Fan Wu ◽  
Lin Zhang ◽  
Shuyang Zhang ◽  
Cao Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Allocation Method

The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.

scholarly journals Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Can Liu ◽  
Zongping Li ◽  
Yueyang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Pollution ◽  
Real Time ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Path Optimization ◽  
Swarm Optimization ◽  
Real Time Traffic

In view of the lack of consideration of environmental protection performance in the traditional path optimization model, a path optimization model for urban traffic networks from the perspective of environmental pollution protection is proposed. Firstly, the urban real-time traffic condition is expressed by the road traffic state index, and an integer programming model is established to optimize the route with the goal of low carbon and shortest distribution time. Then, a hybrid particle swarm optimization algorithm combined with adaptive disturbance mechanism based on variable neighborhood descent is designed, which can better carry out adaptive disturbance according to the situation that the population falls into local extreme value, and the 2-opt local search method is introduced to improve the quality of solution. Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. The experimental demonstration of the proposed model based on two application scenarios shows that its distribution cost, distribution time, and carbon emission are 1975 yuan, 27 h, and 213 kg, respectively, which are better than other comparison models and have high application value.

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