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A Modified NM-PSO Method for Parameter Estimation Problems of Models
An Liu,Erwie Zahara,Ming-Ta Yang
Journal of Applied Mathematics , 2012, DOI: 10.1155/2012/530139
Abstract: Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.
Modified Binary Search Algorithm  [PDF]
Ankit R. Chadha,Rishikesh Misal,Tanaya Mokashi
Computer Science , 2014, DOI: 10.5120/ijais14-451131
Abstract: This paper proposes a modification to the traditional binary search algorithm in which it checks the presence of the input element with the middle element of the given set of elements at each iteration. Modified binary search algorithm optimizes the worst case of the binary search algorithm by comparing the input element with the first & last element of the data set along with the middle element and also checks the input number belongs to the range of numbers present in the given data set at each iteration there by reducing the time taken by the worst cases of binary search algorithm.
A Modified Taboo Search Algorithm for the Job-Shop Problem

TONG Gang,LI Guang-quan,LIU Bao-kun,

系统工程理论与实践 , 2001,
Abstract: In this paper, a modified taboo search algorithm is presented. The algorithm realized to forbid a solution of Job-Shop scheduling problem by encoding the solution visited during the search process and using hashing to keep track of the solution. A back visit strategy is used. The main idea of the strategy is to resume the search from unvisited neighbors of solutions previously generated. The results of computer simulation experiment indicate that the algorithm is applicable and effective.
Modified Grover's Search Algorithm in O(M+logN)Steps  [PDF]
A. S. Gupta,A. Pathak
Physics , 2005,
Abstract: The present letter proposes a modification in the well known Grover's search algorithm, which searches a database of $N$ unsorted items in $O(\sqrt{N/M})$ steps, where $M$ represents the number of solutions to the search problem. Concurrency control techniques and extra registers for marking and storing the solutions are used in the modified algorithm. This requires additional space but it is shown that the use of extra register and marking techniques can reduce the time complexity to $O(M+\log N)$.
Iterative Beam Search for Simple Assembly Line Balancing with a Fixed Number of Work Stations  [PDF]
Christian Blum
Computer Science , 2010,
Abstract: The simple assembly line balancing problem (SALBP) concerns the assignment of tasks with pre-defined processing times to work stations that are arranged in a line. Hereby, precedence constraints between the tasks must be respected. The optimization goal of the SALBP-2 version of the problem concerns the minimization of the so-called cycle time, that is, the time in which the tasks of each work station must be completed. In this work we propose to tackle this problem with an iterative search method based on beam search. The proposed algorithm is able to obtain optimal, respectively best-known, solutions in 283 out of 302 test cases. Moreover, for 9 further test cases the algorithm is able to produce new best-known solutions. These numbers indicate that the proposed iterative beam search algorithm is currently a state-of-the-art method for the SALBP-2.
Autocalibration with the Minimum Number of Cameras with Known Pixel Shape  [PDF]
José I. Ronda,Antonio Valdés,Guillermo Gallego
Computer Science , 2012, DOI: 10.1007/s10851-014-0492-5
Abstract: In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.
Modified Great Deluge for Medical Clustering Problems
Anmar Abuhamdah
International Journal of Emerging Sciences , 2012,
Abstract: Clustering problem is a type of classification under optimization problems, which is considered as a critical area of Data Mining. Medical clustering problem is a type of unsupervised learning in data mining. This work presents great deluge and modified great deluge algorithms for medical clustering problems. The structure of the modified great deluge (MGD) algorithm resembles a great deluge (GD) algorithm structure. The basic difference is that, in MGD the level is updated by a new level that is randomly selected from the list, whilst, in GD the level is initialized only once at the beginning of the search. Therefore, MGD has a better capability of escaping from a local optima compared to GD. Experimental results obtained by two way of minimal distance calculation tested over six benchmark medical datasets show that, MGD is able to produce significantly good quality solutions and outperform some instances of GD algorithm.
Energy Management in Storage-Augmented, Grid-Connected Prosumer Buildings and Neighbourhoods Using a Modified Simulated Annealing Optimization  [PDF]
Rosemarie Velik,Pascal Nicolay
Computer Science , 2015,
Abstract: This article introduces a modified simulated annealing optimization approach for automatically determining optimal energy management strategies in grid-connected, storage-augmented, photovoltaics-supplied prosumer buildings and neighbourhoods based on user-specific goals. For evaluating the modified simulated annealing optimizer, a number of test scenarios in the field of energy self-consumption maximization are defined and results are compared to a gradient descent and a total state space search approach. The benchmarking against these two reference methods demonstrates that the modified simulated annealing approach is able to find significantly better solutions than the gradient descent algorithm - being equal or very close to the global optimum - with significantly less computational effort and processing time than the total state space search approach.
Modified Biogeography-Based Optimization with Local Search Mechanism  [PDF]
Quanxi Feng,Sanyang Liu,Qunying Wu,GuoQiang Tang,Haomin Zhang,Huazhou Chen
Journal of Applied Mathematics , 2013, DOI: 10.1155/2013/960524
Abstract: Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability. To address this limitation, we proposed a modified BBO with local search mechanism (denoted as MLBBO). In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Then, a local search mechanism is used in BBO to supplement with modified migration operator. Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm. The simulation results have been compared with original BBO, DE, improved BBO algorithms, and other evolutionary algorithms. Finally, the performance of the modified migration operator and local search mechanism are also discussed. 1. Introduction In practical application, many problems are regarded as optimization problems. Several effective techniques have been developed for solving optimization problems. Traditional techniques [1, 2] are effective methods for solving these problems, but they need to know the property of problems, such as continuity or differentiability. In the past few decades, various evolutionary algorithms have been sprung up for solving complex optimization problems, for example, genetic algorithm (GA) [3], evolutionary programming (EP) [4], particle swarm optimization (PSO) [5], Ant Colony optimization (ACO) [6], differential evolution (DE) [7], and biogeography-based optimization (BBO) [8]. Compared with traditional techniques, evolutionary algorithms can solve optimization problems without using some information such as differentiability. Biogeography-based optimization (BBO), proposed by Simon [8], is a new entrant in the domain of global optimization based on the theory of biogeography. BBO is developed through simulating the emigration and immigration of species between habitats in the multidimensional solution space, where each habitat represents a candidate solution. Just as species, in biogeography, migrate back and forth between habitats, features in candidate solutions are shared between solutions through migration operator. Good solutions tend to share their features with poor solutions. However, these features, in the origin good solution, may exist in several solutions, both good and poor solutions, which may weaken exploration ability. Several scholars have been working for enhancing the exploration ability. To mention just a few examples, Gong et al. [9] presented a real-coded
Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms

YIN Feng,WANG Yao-Nan,WEI Shu-Ning,

自动化学报 , 2011,
Abstract: A new method for computing numerical solutions to the inverse kinematics problem of robotic manipulators is developed in this paper. With the joint limitations, the electromagnetism-like method (EM) utilizes an attraction-repulsion mechanism to move the sample points towards the optimum solution rapidly. Based on this approximate solution given by EM, a modified Davidon-Fletcher-Powell (DFP) algorithm is developed to solve the problem at the desired precision. Unlike the traditional algorithms, this modified DFP (MDFP) algorithm randomly chooses the search step size between 0 and 1. Hence, the computational complexity is greatly reduced. The experimental results based on ten general test functions and PUMA 560 robot show that this new near-real time hybrid method can produce best performance.
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