scholarly journals Adaptive Central Force Optimization Algorithm Based on the Stability Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Weiyi Qian ◽  
Bo Wang ◽  
Zhiguang Feng
Keyword(s):  
Dynamic Systems ◽  
Gravitational Constant ◽  
Central Force ◽  
Global Optimality ◽  
Time Varying ◽  
Force Optimization ◽  
Adaptive Weight ◽  
The Stability ◽  
Solution Accuracy ◽  
Central Force Optimization

In order to enhance the convergence capability of the central force optimization (CFO) algorithm, an adaptive central force optimization (ACFO) algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.

A parametric family of Massey-type methods: inference, prediction, and sensitivity

2020 ◽  
Vol 16 (3) ◽  
pp. 255-269
Author(s):  
Enrico Bozzo ◽  
Paolo Vidoni ◽  
Massimo Franceschet
Keyword(s):  
Quantitative Analysis ◽  
Control Theory ◽  
Dynamic Systems ◽  
Parametric Family ◽  
Time Varying ◽  
Multi Agent ◽  
Key Features ◽  
The Stability ◽  
Agent Coordination ◽  
Time Aware

AbstractWe study the stability of a time-aware version of the popular Massey method, previously introduced by Franceschet, M., E. Bozzo, and P. Vidoni. 2017. “The Temporalized Massey’s Method.” Journal of Quantitative Analysis in Sports 13: 37–48, for rating teams in sport competitions. To this end, we embed the temporal Massey method in the theory of time-varying averaging algorithms, which are dynamic systems mainly used in control theory for multi-agent coordination. We also introduce a parametric family of Massey-type methods and show that the original and time-aware Massey versions are, in some sense, particular instances of it. Finally, we discuss the key features of this general family of rating procedures, focusing on inferential and predictive issues and on sensitivity to upsets and modifications of the schedule.

An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching

2019 ◽  
Vol 78 (18) ◽  
pp. 26373-26397 ◽  
Author(s):  
Heba M. El-Hoseny ◽  
Zeinab Z. El Kareh ◽  
Wael A. Mohamed ◽  
Ghada M. El Banby ◽  
Korany R. Mahmoud ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Central Force ◽  
Medical Image Fusion ◽  
Force Optimization ◽  
Histogram Matching ◽  
Fusion Approach ◽  
Central Force Optimization

scholarly journals An Improved Central Force Optimization Algorithm for Multimodal Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Liu ◽  
Yu-ping Wang
Keyword(s):  
Central Force ◽  
Multimodal Optimization ◽  
High Dimensions ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Clustering Technique ◽  
Force Optimization ◽  
Comparison Results ◽  
Complex Statistical Analysis ◽  
Central Force Optimization

This paper proposes the hybrid CSM-CFO algorithm based on the simplex method (SM), clustering technique, and central force optimization (CFO) for unconstrained optimization. CSM-CFO is still a deterministic swarm intelligent algorithm, such that the complex statistical analysis of the numerical results can be omitted, and the convergence intends to produce faster and more accurate by clustering technique and good points set. When tested against benchmark functions, in low and high dimensions, the CSM-CFO algorithm has competitive performance in terms of accuracy and convergence speed compared to other evolutionary algorithms: particle swarm optimization, evolutionary program, and simulated annealing. The comparison results demonstrate that the proposed algorithm is effective and efficient.

scholarly journals On Some Sufficiency-Type Global Stability Results for Time-Varying Dynamic Systems with State-Dependent Parameterizations

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
M. De la Sen
Keyword(s):  
Global Stability ◽  
Dynamic Systems ◽  
Taylor Series Expansion ◽  
Time Varying ◽  
State Dependent ◽  
The Matrix ◽  
The Stability ◽  
Interval Type ◽  
Truncation Order ◽  
Stability Results

This paper formulates sufficiency-type global stability and asymptotic stability results for, in general, nonlinear time-varying dynamic systems with state-trajectory solution-dependent parameterizations. The stability proofs are based on obtaining sufficiency-type conditions which guarantee that either the norms of the solution trajectory or alternative interval-type integrals of the matrix of dynamics of the higher-order than linear terms do not grow faster than their available supremum on the preceding time intervals. Some extensions are also given based on the use of a truncated Taylor series expansion of chosen truncation order with multiargument integral remainder for the dynamics of the differential system.

Structural Decomposition Approach for the Stability of Uncertain Dynamic Systems

1988 ◽  
Vol 55 (4) ◽  
pp. 992-994 ◽  
Author(s):  
Y. H. Chen ◽  
Chieh Hsu
Keyword(s):  
Dynamic Systems ◽  
A Priori ◽  
Parameter Variation ◽  
Stability Study ◽  
Fast Time ◽  
Time Varying ◽  
Decomposition Approach ◽  
New Approach ◽  
Uncertain Dynamic Systems ◽  
The Stability

The stability property for a class of dynamic systems with uncertain parameter variation is studied. The uncertainty can be fast time-varying and unpredictable. A new approach for stability study is proposed. The only required information on the uncertain variation is its possible bound as well as structure. That is, no a priori knowledge on the realization of the variation is needed.

Optimal stable gait for nonlinear uncertain humanoid robot using central force optimization algorithm

2019 ◽  
Vol 36 (2) ◽  
pp. 599-621 ◽  
Author(s):  
Tran Thien Huan ◽  
Ho Pham Huy Anh
Keyword(s):  
Differential Evolution ◽  
Humanoid Robot ◽  
Differential Evolution Algorithm ◽  
Humanoid Robots ◽  
Central Force ◽  
Content Type ◽  
Force Optimization ◽  
Gait Parameters ◽  
Stable Gait ◽  
Central Force Optimization

Purpose The purpose of this paper is to design a novel optimized biped robot gait generator which plays an important role in helping the robot to move forward stably. Based on a mathematical point of view, the gait design problem is investigated as a constrained optimum problem. Then the task to be solved is closely related to the evolutionary calculation technique. Design/methodology/approach Based on this fact, this paper proposes a new way to optimize the biped gait design for humanoid robots that allows stable stepping with preset foot-lifting magnitude. The newly proposed central force optimization (CFO) algorithm is used to optimize the biped gait parameters to help a nonlinear uncertain humanoid robot walk robustly and steadily. The efficiency of the proposed method is compared with the genetic algorithm, particle swarm optimization and improved differential evolution algorithm (modified differential evolution). Findings The simulated and experimental results carried out on the small-sized nonlinear uncertain humanoid robot clearly demonstrate that the novel algorithm offers an efficient and stable gait for humanoid robots with respect to accurate preset foot-lifting magnitude. Originality/value This paper proposes a new algorithm based on four key gait parameters that enable dynamic equilibrium in stable walking for nonlinear uncertain humanoid robots of which gait parameters are initiatively optimized with CFO algorithm.

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