scholarly journals Multicamera Calibration Optimization Method Based on Improved Seagull Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shuai Du ◽  
Jianyu Wang ◽  
Jia Guo
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Fusion Algorithm ◽  
Local Optima ◽  
Internal Parameters ◽  
Reprojection Error ◽  
Adaptive Differential Evolution ◽  
Calibration Accuracy

There are some problems in the process of camera calibration, such as insufficient accuracy and poor accuracy. Based on the seagull algorithm, the adaptive differential evolution algorithm is combined with the seagull algorithm to optimize the multicamera calibration. The seagull algorithm can achieve good results on multiparameter problems and effectively avoid falling into local optima. In this paper, the adaptive differential search algorithm is adopted to improve the local search ability and optimize the local search and global search ability. According to Zhang Zhengyou's method, the calibrated parameter is obtained, in which the parameter is used as the initial value. Then, taking the minimum mean error as the criterion, the improved seagull algorithm (SOA-SaDE) is used to establish the objective function, and the internal parameters and distortion coefficient of the camera are further solved. Verification experiments showed that the fusion algorithm has less reprojection error and higher calibration accuracy gull algorithm.

Development of Foot Structure for Humanoid Robot Using Topology Optimization

2018 ◽  
Vol 29 ◽  
pp. 34-45
Author(s):  
Van Tinh Nguyen ◽  
Daichi Kiuchi ◽  
Hiroshi Hasegawa
Keyword(s):  
Topology Optimization ◽  
Humanoid Robot ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Gait Pattern ◽  
Surface Model ◽  
Foot Structure ◽  
Adaptive Differential Evolution ◽  
Reaction Forces ◽  
Set Up

This paper addresses the development of a foot structure for 22-Degree of Freedom (DoF) humanoid robot. The goal of this research is to reduce the weight of the foot and enable the robot to walk steadily. The proposed foot structure is based on the consideration of cases where the ground reaction forces are set up in different situations. The optimal foot structure is a combination of all the topology optimization results. Additionally, a gait pattern is generated by an approximated optimization method based on Response Surface Model (RSM) and Improved Self-Adaptive Differential Evolution Algorithm (ISADE). The result is validated through dynamic simulation by a commercially available software called Adams (MSC software, USA) with the humanoid robot named KHR-3HV belonging to Kondo Kagaku company.

P-lsGOF: A parallel learning-selection-based global optimization framework

2020 ◽  
Vol 39 (5) ◽  
pp. 7333-7361
Author(s):  
Mingcheng Zuo ◽  
Guangming Dai
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Principal Component ◽  
Parallel Learning ◽  
Optimization Framework ◽  
Engineering Design Problems ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

When optimizing complicated engineering design problems, the search spaces are usually extremely nonlinear, leading to the great difficulty of finding optima. To deal with this challenge, this paper introduces a parallel learning-selection-based global optimization framework (P-lsGOF), which can divide the global search space to numbers of sub-spaces along the variables learned from the principal component analysis. The core search algorithm, named memory-based adaptive differential evolution algorithm (MADE), is parallel implemented in all sub-spaces. MADE is an adaptive differential evolution algorithm with the selective memory supplement and shielding of successful control parameters. The efficiency of MADE on CEC2017 unconstrained problems and CEC2011 real-world problems is illustrated by comparing with recently published state-of-the-art variants of success-history based adaptative differential evolution algorithm with linear population size reduction (L-SHADE) The performance of P-lsGOF on CEC2011 problems shows that the optimized results by individually conducting MADE can be further improved.

Application of Charge System Search Algorithm for Data Clustering

2016 ◽  
pp. 383-399
Author(s):  
Yugal Kumar ◽  
Gadadhar Sahoo
Keyword(s):  
Local Search ◽  
Data Clustering ◽  
Search Algorithm ◽  
Optimization Method ◽  
Experimental Results ◽  
Second Law ◽  
Charge System ◽  
Charged System Search ◽  
Charged System ◽  
Optimal Cluster

This chapter presents a charged system search (CSS) optimization method for finding the optimal cluster centers for a given dataset. In CSS algorithm, while the Coulomb and Gauss laws from electrostatics are applied to initiate the local search, global search is performed using Newton second law of motion from mechanics. The efficiency and capability of the proposed algorithm is tested on seven datasets and compared with existing algorithms like K-Means, GA, PSO and ACO. From the experimental results, it is found that the proposed algorithm provides more accurate and effective results in comparison to other existing algorithms.

A New Variant Harmony Search Algorithm with Random Mutation Strategy

2014 ◽  
Vol 1065-1069 ◽  
pp. 3425-3428
Author(s):  
Xiu Hong Zhao
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Random Mutation ◽  
Local Optima ◽  
Intelligent Optimization ◽  
New Variant ◽  
Mutation Strategy

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method, which has been paid much attention recently. However, intelligent optimization methods are easily trapped into local optima, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm with random mutation strategy (HSRM) is proposed in this paper. The HSRM uses a random mutation strategy to replace the pitch adjusting operation, and dynamically adjust the key parameter pitch adjusting rate (PAR). Experiment results demonstrated that the proposed method is superior to the HS and recently developed variants (IHS, and GHS) and other meta-heuristic algorithm.

scholarly journals Highly Precise Modified Blue Whale Method Framed by Blending Bat and Local Search Algorithm for the Optimality of Image Fusion Algorithm

2020 ◽  
Vol 2 (4) ◽  
pp. 195-208
Author(s):  
Sayantan Dutta ◽  
Ayan Banerjee
Keyword(s):  
Local Search ◽  
Image Fusion ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Whale Optimization Algorithm ◽  
Local Search Algorithm ◽  
Fusion Algorithm ◽  
Whale Optimization ◽  
Nature Inspired Algorithm

Image fusion has gained huge popularity in the field of medical and satellite imaging for image analysis. The lack of usages of image fusion is due to a deficiency of suitable optimization techniques and dedicated hardware. In recent days WOA (whale optimization algorithm) is gaining popularity. Like another straightforward nature-inspired algorithm, WOA has some problems in its searching process. In this paper, we have tried to improve the WOA algorithm by modifying the WOA algorithm. This MWOA (modified whale optimization algorithm) algorithm is amalgamed with LSA (local search algorithm) and BA (bat algorithm). The LSA algorithm helps the system to be faster, and BA algorithm helps to increase the accuracy of the system. This optimization algorithm is checked using MATLAB R2018b. Simulated using ModelSim, and the synthesizing is done using Xilinx Vivado 18.2 synthesis tool. The outcome of the simulation result and the synthesis result outshine other metaheuristic optimization algorithms.

A New Hybrid Binary-Real Coded Cuckoo Search and Tabu Search Algorithm for Solving the Unit-Commitment Problem

2021 ◽  
Vol 10 (2) ◽  
pp. 104-119
Author(s):  
Amel Terki ◽  
Hamid Boubertakh
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Unit Commitment Problem ◽  
Local Optima

This paper proposes a new intelligent optimization approach to deal with the unit commitment (UC) problem by finding the optimal on/off states strategy of the units under the system constraints. The proposed method is a hybridization of the cuckoo search (CS) and the tabu search (TS) optimization techniques. The former is distinguished by its efficient global exploration mechanism, namely the levy flights, and the latter is a successful local search method. For this sake, a binary code is used for the status of units in the scheduled time horizon, and a real code is used to determine the generated power by the committed units. The proposed hybrid CS and TS (CS-TS) algorithm is used to solve the UC problem such that the CS guarantees the exploration of the whole search space, while the TS algorithm deals with the local search in order to avoid the premature convergence and prevent from trapping into local optima. The proposed method is applied to the IEEE standard systems of different scales ranging from 10 to 100 units. The results show clearly that the proposed method gives better quality solutions than the existing methods.

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