SURFACE EFFECT CORRECTION OF MOISTURE DETERMINATION BY NEUTRON PROBE USING PSO TECHNIQUE AND MCNP

2009 ◽  
Vol 06 (02) ◽  
pp. 247-255
Author(s):  
ALI ASGHAR MOWLAWI ◽  
HADI SADOGHI YAZDI ◽  
MEHDI ARGHIANI ◽  
JABER ROOHI ◽  
RAHIM KOOHI-FAYEGH ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Surface Effect ◽  
Soil Surface ◽  
Moisture Determination ◽  
Swarm Optimization ◽  
Convex Optimization Problem ◽  
Neutron Probe ◽  
Decision Variables ◽  
Paraffin Block

The usefulness of the neutron meter is limited for moisture measurement near the soil surface. In this present work, optimum dimension of a paraffin block has been calculated to correct the surface effect in order to use neutron probe near the soil surface by MCNP4C code and Particle Swarm Optimization (PSO) technique. PSO is chiefly a technique to find a global or quasi-minimum for a nonlinear and non-convex optimization problem, and there have been few studies into optimization problems with discrete decision variables. The results show a paraffin block 23.55 × 23.55 cm2 square base with 4.84, 4.92, 5.10, 5.23, and 5.47 cm thickness which can correct the surface effect fairly for 0.10, 0.20, 0.30, 0.40, and 0.50 g/g moisture.

Two Meta-Heuristics for Solving Unconstrained Optimization Problems and Machinery Problems

2014 ◽  
Vol 1044-1045 ◽  
pp. 1418-1423
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Finite Sequence ◽  
Swarm Optimization ◽  
Machining Optimization

Machining optimization problem aims to optimize machinery conditions which are important for economic settings. The effective methods for solving these problems using a finite sequence of instructions can be categorized into two groups; exact optimization algorithm and meta-heuristic algorithms. A well-known meta-heuristic approach called Harmony Search Algorithm was used to compare with Particle Swarm Optimization. We implemented and analysed algorithms using unconstrained problems under different conditions included single, multi-peak, curved ridge optimization, and machinery optimization problem. The computational outputs demonstrated the proposed Particle Swarm Optimization resulted in the better outcomes in term of mean and variance of process yields.

Glowworm Swarm Optimization (GSO) Algorithm for Optimization Problems: A State-of-the-Art Review

2013 ◽  
Vol 421 ◽  
pp. 507-511 ◽  
Author(s):  
Nurezayana Zainal ◽  
Azlan Mohd Zain ◽  
Nor Haizan Mohamed Radzi ◽  
Amirmudin Udin
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Global Optimum ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Multimodal Function Optimization ◽  
Glowworm Swarm Optimization ◽  
Multimodal Function ◽  
Optimum Solution

Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and slow speed to converge. This paper reviews the exposition of a new method of swarm intelligence in solving optimization problems using GSO. Recently the GSO algorithm was used simultaneously to find solutions of multimodal function optimization problem in various fields in today industry such as science, engineering, network and robotic. From the paper review, we could conclude that the basic GSO algorithm, GSO with modification or improvement and GSO with hybridization are considered by previous researchers in order to solve the optimization problem. However, based on the literature review, many researchers applied basic GSO algorithm in their research rather than others.

scholarly journals Multivariate Spectral Gradient Algorithm for Nonsmooth Convex Optimization Problems

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Yaping Hu
Keyword(s):  
Convex Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Gradient Algorithm ◽  
Nonsmooth Convex Optimization ◽  
Convex Optimization Problem ◽  
Convex Optimization Problems ◽  
Yosida Regularization ◽  
Approximate Function ◽  
Original Objective

We propose an extended multivariate spectral gradient algorithm to solve the nonsmooth convex optimization problem. First, by using Moreau-Yosida regularization, we convert the original objective function to a continuously differentiable function; then we use approximate function and gradient values of the Moreau-Yosida regularization to substitute the corresponding exact values in the algorithm. The global convergence is proved under suitable assumptions. Numerical experiments are presented to show the effectiveness of this algorithm.

A Heuristic Algorithm Based on Leadership Strategy: Leader of Dolphin Herd Algorithm (LDHA)

2015 ◽  
Vol 19 (4) ◽  
pp. 491-499 ◽  
Author(s):  
Jianqiang Zhao ◽  
◽  
Kao Ge ◽  
Kangyao Xu
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Complex Function ◽  
Predatory Behavior ◽  
Function Optimization ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Leadership Strategy ◽  
Complex Functions

A heuristic algorithm named the leader of dolphin herd algorithm (LDHA) is proposed in this paper to solve an optimization problem whose dimensionality is not high, with dolphins that imitate predatory behavior. LDHA is based on a leadership strategy. Using the leadership strategy as reference, we have designed the proposed algorithm by simulating the preying actions of dolphin herds. Several intelligent behaviors, such as “producing leaders,” “group gathering,” “information sharing,” and “rounding up prey,” are abstracted by LDHA. The proposed algorithm is tested on 15 typical complex function optimization problems. The testing results reveal that compared with the particle swarm optimization and the genetic algorithms, LDHA has relatively high optimization accuracy and capability for complex functions. Further, it is almost unaffected by the inimicality, multimodality, or dimensions of functions in the function optimization section, which implies better convergence. In addition, ultra-high-dimensional function optimization capabilities of this algorithm were tested using the IEEE CEC 2013 global optimization benchmark. Unfortunately, the proposed optimization algorithm has a limitation in that it is not suitable for ultra-high-dimensional functions.

Improved Harmony Search Algorithm for Solving Optimization Problem with Mixed Discrete Variables

2013 ◽  
Vol 325-326 ◽  
pp. 1485-1488
Author(s):  
Shi Ming Hao ◽  
Li Zhi Cheng
Keyword(s):  
Constrained Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Continuous Variables ◽  
Discrete Variables ◽  
Decision Variables

The classical harmony search algorithm (HSA) can only be used to solve the unconstrained optimization problems with continuous decision variables. Therefore, the classical HSA is not suitable for solving an engineering optimization problem with mixed discrete variables. In order to improve the classical HSA, an engineering method for dealing with mixed discrete decision variables is introduced and an exact non-differentiable penalty function is used to transform the constrained optimization design model into an unconstrained mathematical model. Based on above improvements, a program of improved HSA is designed and it can be used for solving the constrained optimization design problems with continuous variables, integer variables and non-equidistant discrete variables. Finally, an optimization design example of single-stage cylindrical-gear reducer with mixed-discrete variables is given. The example shows that the designed program runs steadily and the proposed method is effective in engineering design.

Counterweight Balancing for Reducing Machine Frame Vibration: A Convex Optimization Framework

2006 ◽  
Author(s):  
Myriam Verschuure ◽  
Bram Demeulenaere ◽  
Jan Swevers ◽  
Joris De Schutter
Keyword(s):  
Convex Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Global Optimum ◽  
Convex Optimization Problem ◽  
Optimization Framework ◽  
Convex Optimization Problems ◽  
Drive Speed ◽  
Maximal Reduction

This paper focusses on reducing, through counterweight addition, the vibration of an elastically mounted, rigid machine frame that supports a linkage. In order to determine the counterweights that yield a maximal reduction in frame vibration, a non-linear optimization problem is formulated with the frame kinetic energy as objective function and such that a convex optimization problem is obtained. Convex optimization problems are nonlinear optimization problems that have a unique (global) optimum, which can be found with great efficiency. The proposed methodology is successfully applied to improve the results of the benchmark four-bar problem, first considered by Kochev and Gurdev. For this example, the balancing is shown to be very robust for drive speed variations and to benefit only marginally from using a coupler counterweight.

An Algorithm for Continuous Optimization Problems using Hybrid Particle Updating Method

2016 ◽  
Vol 3 (1) ◽  
pp. 164
Author(s):  
Peter Bamidele Shola ◽  
L B Asaju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Population Based ◽  
Global Optimum ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Continuous Optimization Problems ◽  
Number Of Iterations

<p>Optimization problem is one such problem commonly encountered in many area of endeavor, obviously due to the need to economize the use of the available resources in many problems. This paper presents a population-based meta-heuristic algorithm   for solving optimization problems in a continous space. The algorithm, combines a form of cross-over technique with a position updating formula based on the instantaneous global best position to update each particle position .The algorithm was tested and compared with the standard particle swarm optimization (PSO)  on many benchmark functions. The result suggests a better performance of the algorithm over the later in terms of reaching (attaining) the global optimum value (at least for those benchmark functions considered) and the rate of convergence in terms of the number of iterations required reaching the optimum values.</p>

CCPSO Based on PCA for Large-Scale Optimization Problem

2012 ◽  
Vol 236-237 ◽  
pp. 1190-1194
Author(s):  
Wen Hua Han ◽  
Xu Chen ◽  
Jun Xu
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Principal Component ◽  
Complex Problem ◽  
Large Scale Optimization ◽  
Multimodal Optimization ◽  
Swarm Optimization ◽  
Gaussian Distributions ◽  
Scale Optimization

This paper proposed a cooperative coevolving particle swarm optimization base on principal component analysis (PCA-CCPSO) algorithm for large-scale and complex problem. In this algorithm, PCA are used to pick up the available particles which gathered the important information of the initialized particles for CCPSO. The Cauchy and Gaussian distributions are used to update the position of the particles and the coevolving subcomponent size of the particles is determined dynamically. The experimental results demonstrate that the convergence speed of PCA-CCPSO is faster than that of CCPSO in solving the large-scale and complex multimodal optimization problems.

scholarly journals Designing Phononic Crystals With Convex Optimization

2013 ◽  
Author(s):  
Han Men ◽  
Robert M. Freund ◽  
Ngoc C. Nguyen ◽  
Joel Saa-Seoane ◽  
Jaime Peraire
Keyword(s):  
Linear Programming ◽  
Convex Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Phononic Crystals ◽  
Semidefinite Program ◽  
Constraint Generation ◽  
Decision Variables ◽  
New Formulation ◽  
Novel Algorithm

Designing phononic crystals by creating frequency bandgaps is of particular interest in the engineering of elastic and acoustic microstructured materials. Mathematically, the problem of optimizing the frequency bandgaps is often nonconvex, as it requires the maximization of the higher indexed eigenfrequency and the minimization of the lower indexed eigenfrequency. A novel algorithm [1] has been previously developed to reformulate the original nonlinear, nonconvex optimization problem to an iteration-specific semidefinite program (SDP). This algorithm separates two consecutive eigenvalues — effectively maximizing bandgap (or bandwidth) — by separating the gap between two orthogonal subspaces, which are comprised columnwise of “important” eigenvectors associated with the eigenvalues being bounded. By doing so, we avoid the need of computation of eigenvalue gradient by computing the gradient of affine matrices with respect to the decision variables. In this work, we propose an even more efficient algorithm based on linear programming (LP). The new formulation is obtained via approximation of the semidefinite cones by judiciously chosen linear bases, coupled with “delayed constraint generation”. We apply the two convex conic formulations, namely, the semidefinite program and the linear program, to solve the bandgap optimization problems. By comparing the two methods, we demonstrate the efficacy and efficiency of the LP-based algorithm in solving the category of eigenvalue bandgap optimization problems.

Significant Factors Identification for Particle Swarm Optimization Algorithm to Solving the Design Optimization Problem of a Four-Bar Linkage for Path Generation

2012 ◽  
Vol 249-250 ◽  
pp. 1180-1187 ◽  
Author(s):  
Cheng Kang Lee ◽  
Yung Chang Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Path Generation ◽  
Swarm Optimization ◽  
Control Factors ◽  
Four Bar Linkage

Particle swarm optimization (PSO) is a well-known population-based searching algorithm to solving optimization problems. This paper aims at identifying significant control factors for PSO to solving the design optimization problem of a four-bar linkage for path generation. Control factors considered herein are inertial weight, acceleration coefficients, breeding operation, and the number of population. A full factorial design of experiments is used to construct a set of experiments. Experimental results are analyzed with the analysis of variance method. According to the results obtained in this paper, breeding operation and the interaction between breeding operation and acceleration coefficients are significant. Inertial weight, acceleration coefficients, the number of population, and the other interactions are not significant. For the design optimization problem discussed herein, it is suggested to adopt breeding operation strategy and apply constant acceleration coefficients to increase significantly PSO’s performance and robustness. Type of inertial weight and the number of population do not affect PSO’s performance and robustness significantly.

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