scholarly journals The Comparison of Performance between Contrast Source Inversion and Its Related Algorithms and Several Improved Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Meng Wang ◽  
Guizhen Lu
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Scattering Problem ◽  
The State ◽  
Convergence Speed ◽  
Source Inversion ◽  
Contrast Source Inversion ◽  
Data Error ◽  
State Error

The contrast source inversion (CSI) is an effective method for solving microwave imaging problems which is widely utilized. The core of the CSI is to change the conventional inverse scattering problem into an optimization problem. The two items in the objective function describe the state error and data error, respectively. As it is all known, there is almost no complete performance comparison based on Fresnel data for the CSI and its related improved algorithms. In addition, the performance of the algorithm under different weights was not analyzed before and the convergence speed of original CSI is slow. Firstly, this paper compares the performance of traditional CSI and its improved algorithms from three aspects of qualitative imaging effect, convergence speed, and objective function value based on Fresnel data. Secondly, the influence of the state error and the data error under different weights on the convergence rate and the objective function value are studied. For the limitation of a slower convergence rate, the CSI with weights (W-CSI), the CSI with dynamic reduction factor (CSI-DRF), and its related algorithms, which can get better convergence rate compared with their relative original algorithms, are proposed. Eventually, the future research work is prospected.

Modified Harmony Search Algorithm for 0-1 Knapsack Problems

2013 ◽  
Vol 365-366 ◽  
pp. 182-185
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Convergence And Stability ◽  
Update Strategy ◽  
Harmony Memory

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

Unscented Particle Filter Algorithm for Ballistic Target Tracking

2011 ◽  
Vol 130-134 ◽  
pp. 369-372
Author(s):  
Jun Wei Zhao ◽  
Ming Jun Zhang ◽  
Yong Gang Yan ◽  
Yong Peng Yan
Keyword(s):  
Convergence Rate ◽  
Particle Filter ◽  
Target Tracking ◽  
State Vector ◽  
The State ◽  
Convergence Speed ◽  
Matlab Software ◽  
Unscented Particle Filter ◽  
Simulation Results ◽  
Particle Filter Algorithm

At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.

scholarly journals Convergence Rate Analysis of the Proximal Difference of the Convex Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Xueyong Wang ◽  
Ying Zhang ◽  
Haibin Chen ◽  
Xipeng Kou
Keyword(s):  
Convergence Rate ◽  
Convex Function ◽  
Objective Function ◽  
Convex Functions ◽  
Objective Function Value ◽  
Convex Decomposition ◽  
Rate Analysis ◽  
Difference Of Convex ◽  
The Difference ◽  
Convergence Rate Analysis

In this paper, we study the convergence rate of the proximal difference of the convex algorithm for the problem with a strong convex function and two convex functions. By making full use of the special structure of the difference of convex decomposition, we prove that the convergence rate of the proximal difference of the convex algorithm is linear, which is measured by the objective function value.

Unscented Particle Filter Algorithm for Ballistic Target Tracking

2014 ◽  
Vol 686 ◽  
pp. 359-362
Author(s):  
Yan Zhai ◽  
Xiao Bo Guo ◽  
Yong Gang Yan
Keyword(s):  
Convergence Rate ◽  
Particle Filter ◽  
Target Tracking ◽  
State Vector ◽  
The State ◽  
Convergence Speed ◽  
Matlab Software ◽  
Unscented Particle Filter ◽  
Simulation Results ◽  
Particle Filter Algorithm

At present, the ballistic Target tracking has a higher demand in convergence rate and tracking precision of filter algorithm. In the paper, a filter algorithm was improved based on particle filter. The algorithm was carried out from the aspects such as particle degradation and particle diversity lack. A novel ballistic coefficient parameter model was built, and was expanded to the state vector for filtering. Finally, the improved algorithm was simulated by MATLAB software. The simulation results show that the algorithm can obtain better convergence speed and tracking precision.

A Self-Study Harmony Search Algorithm for Optimization Problems

2014 ◽  
Vol 1006-1007 ◽  
pp. 1017-1020
Author(s):  
Ping Zhang ◽  
Mei Ling Li ◽  
Qian Han ◽  
Guo Jun Li
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Optimization Problems ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Unconstrained Optimization Problems ◽  
Self Study ◽  
Harmony Memory

A self-study harmony search (SSHS) algorithm for solving unconstrained optimization problems has presented in this paper . SSHS employs a novel self-study strategy to generate new solution vectors which can enhance accuracy and convergence rate of harmony search (HS) algorithm. SSHS algorithm as proposed, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. a large number of experiments improved that SSHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS)

scholarly journals The Comparison of Lately Proposed Harris Hawks Optimization and Jaya Optimization in Solving Directional Overcurrent Relays Coordination Problem

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Jiangtao Yu ◽  
Chang-Hwan Kim ◽  
Sang-Bong Rhee
Keyword(s):  
Nonlinear Programming ◽  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Cooperative Behavior ◽  
Mixed Integer ◽  
Coordination Problem ◽  
Computation Efficiency ◽  
Design Variables ◽  
Overcurrent Relays

In this paper, a lately proposed Harris Hawks Optimizer (HHO) is used to solve the directional overcurrent relays (DOCRs) coordination problem. To the best of the authors’ knowledge, this is the first time HHO is being used in the DOCRs coordination problem. The main inspiration of HHO is the cooperative behavior and chasing style of Harris’ hawks from different directions, based on the dynamic nature of scenarios and escaping patterns of the prey. To test its performances in solving the DOCRs coordination problem, it is adopted in 3-bus, 4-bus, 8-bus, and 9-bus systems, which are formulated by three kinds of optimization models as linear programming (LP), nonlinear programming (NLP), and mixed integer nonlinear programming (MINLP), according to the nature of the design variables. Meanwhile, another lately proposed optimization algorithm named Jaya is also adopted to solve the same problem, and the results are compared with HHO in aspects of objective function value, convergence rate, robustness, and computation efficiency. The comparisons show that the robustness and consistency of HHO is relatively better than Jaya, while Jaya provides faster convergence rate with less CPU time and occasionally more competitive objective function value than HHO.

Non Linear State and Parameters Estimation in Chemical Processes: Analysis and Improvement of Three Estimation Structures Applied to a CSTR

2011 ◽  
Vol 9 (1) ◽  
Author(s):  
Héctor Botero ◽  
Hernán Álvarez
Keyword(s):  
Parameter Estimation ◽  
Chemical Process ◽  
Chemical Processes ◽  
The State ◽  
Convergence Speed ◽  
Parameters Estimation ◽  
State Variables ◽  
Input Output ◽  
On Line ◽  
Linear State

This paper proposes a new composite observer capable of estimating the states and unknown (or changing) parameters of a chemical process, using some input-output measurements, the phenomenological based model and other available knowledge about the process. The proposed composite observer contains a classic observer (CO) to estimate the state variables, an observer-based estimator (OBE) to obtain the actual values of the unknown or changing parameters needed to tune the CO, and an asymptotic observer (AO) to estimate the states needed as input to the OBE. The proposed structure was applied to a CSTR model with three state variables. With the proposed structure, the concentration of reactants and other CSTR parameters can be estimated on-line if the reactor and jacket temperatures are known. The procedure for the design of the proposed structure is simple and guarantees observer convergence. In addition, the convergence speed of state and parameter estimation can be adjusted independently.

scholarly journals Chip Attach Scheduling in Semiconductor Assembly

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Zhicong Zhang ◽  
Kaishun Hu ◽  
Shuai Li ◽  
Huiyu Huang ◽  
Shaoyong Zhao
Keyword(s):  
Objective Function ◽  
Objective Function Value ◽  
Learning Algorithm ◽  
Parallel Machine Scheduling ◽  
Volume Index ◽  
Production Volume ◽  
Learning Problem ◽  
Reward Function ◽  
Unrelated Parallel Machine Scheduling ◽  
Target Production

Chip attach is the bottleneck operation in semiconductor assembly. Chip attach scheduling is in nature unrelated parallel machine scheduling considering practical issues, for example, machine-job qualification, sequence-dependant setup times, initial machine status, and engineering time. The major scheduling objective is to minimize the total weighted unsatisfied Target Production Volume in the schedule horizon. To apply Q-learning algorithm, the scheduling problem is converted into reinforcement learning problem by constructing elaborate system state representation, actions, and reward function. We select five heuristics as actions and prove the equivalence of reward function and the scheduling objective function. We also conduct experiments with industrial datasets to compare the Q-learning algorithm, five action heuristics, and Largest Weight First (LWF) heuristics used in industry. Experiment results show that Q-learning is remarkably superior to the six heuristics. Compared with LWF, Q-learning reduces three performance measures, objective function value, unsatisfied Target Production Volume index, and unsatisfied job type index, by considerable amounts of 80.92%, 52.20%, and 31.81%, respectively.

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