A Self-Adaptive and Variable Step Length Alopex Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 4302-4307
Author(s):  
Dong Li
Keyword(s):  
Optimal Solution ◽  
Step Length ◽  
Global Optimal Solution ◽  
Optimum Algorithm ◽  
Variable Step ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Simulation Results ◽  
Self Adaptive

Alopex is a heuristic and optimum algorithm. A self-adaptive and variable step length Alopex algorithm was raised to exceed local optimal solution and to approximate global optimal solution based on modified Alopex algorithm. To improve modified Alopex approximation precision further and eliminate the follow-on shocks appearance, the reasonable alter of δin was implemented. The simulation results show algorithm optimized is practicable and effective.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

The Research on Modified K-Means Algorithm Based on GA&SA

2013 ◽  
Vol 347-350 ◽  
pp. 3242-3246
Author(s):  
Zhe Feng Zhu ◽  
Xiao Bin Hui ◽  
Yi Qian Cao ◽  
Wan Xiang Lian
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Search ◽  
Local Optimization ◽  
Effective Algorithm ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Clustering Center

The traditional K-means clustering algorithm has the disadvantage of weakness in overall search, easily falling into local optimization, highly reliance on initial clustering center. Aiming at the drawback of falling into partial optimization, putting forward a modified K-means algorithm mixing GA and SA, which combined the advantages of global search ability of GA and local search, to avoid K-means algorithm to lost into local optimal solution. The results of simulation show that the performance of above-mentioned algorithm is better in the optimization capacity than before, and easier to get the global optimal solution. It is an effective algorithm.

Genetic Algorithm Based on Permutation Distance for Laser Antimissile Problem

2010 ◽  
Vol 40-41 ◽  
pp. 488-493
Author(s):  
Yong Sun ◽  
Mao Rui Zhang ◽  
Wei Wei Liu ◽  
Li Na Zhang ◽  
He Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
The Real ◽  
Real Application ◽  
Global Optimal ◽  
Simulation Results

The genetic algorithm based on permutation distance definition is used to solve the laser antimissile system. When faced with multiple attacking targets, it is clearly important for the laser antimissile system to determine the sequence of the attacking targets to be intercepted so that the maximum attacking targets are destroyed. It’s very difficult to find the global optimal solution, especially when the number of the targets is greater than six. The permutation distance definition is introduced to measure the distribution of the population. The successive zeros permutation distance is to stop the genetic algorithm iterations. Finally, taking ten targets as an example, the simulation results show that the convergence of the algorithm is fast and this achievement can be used in the real application.

A New BP Algorithm and its Application

2013 ◽  
Vol 811 ◽  
pp. 413-416
Author(s):  
Fang Liu ◽  
Yue Guang Li
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Network Structure ◽  
Binary Code ◽  
Optimal Solution ◽  
Bp Algorithm ◽  
Global Optimal Solution ◽  
The Best Approximation ◽  
Global Optimal ◽  
Simulation Results

In this paper, based on the combination of Genetic algorithm and BP algorithm, a new algorithm is proposed in this paper. The BP operator is embedded in the genetic operation in the algorithm, the algorithm effectively assimilates the global optimization of genetic algorithm and fast convergence of BP algorithm, and it encodes the construction and the weights hybrid with real code and binary code, achieving the same step optimization of structure and weights. The simulation results show that, the new algorithm can quickly converge to the global optimal solution, but also can obtain the best approximation of weights in the network structure.

scholarly journals On Locally and Globally Optimal Solutions in Scalar Variational Control Problems

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 829 ◽  
Author(s):  
Savin Treanţă
Keyword(s):  
Control Problem ◽  
Optimal Solution ◽  
Control Problems ◽  
Optimal Solutions ◽  
Global Optimal Solution ◽  
New Class ◽  
Minimal Criterion ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Curvilinear Integral

In this paper, optimality conditions are studied for a new class of PDE and PDI-constrained scalar variational control problems governed by path-independent curvilinear integral functionals. More precisely, we formulate and prove a minimal criterion for a local optimal solution of the considered PDE and PDI-constrained variational control problem to be its global optimal solution. The effectiveness of the main result is validated by a two-dimensional non-convex scalar variational control problem.

AN OPTIMIZATION APPROACH TO LOCATING AND STABILIZING UNSTABLE PERIODIC ORBITS OF CHAOTIC SYSTEMS

2002 ◽  
Vol 12 (05) ◽  
pp. 1163-1172 ◽  
Author(s):  
YU-PING TIAN
Keyword(s):  
Periodic Orbits ◽  
Chaotic Systems ◽  
Main Idea ◽  
Optimal Solution ◽  
Optimization Process ◽  
Optimization Approach ◽  
Unstable Periodic Orbits ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal

In this paper, a novel method for locating and stabilizing inherent unstable periodic orbits (UPOs) in chaotic systems is proposed. The main idea of the method is to formulate the UPO locating problem as an optimization issue by using some inherent properties of UPOs of chaotic systems. The global optimal solution of this problem yields the desired UPO. To avoid a local optimal solution, the state of the controlled chaotic system is absorbed into the initial condition of the optimization problem. The ergodicity of chaotic dynamics guarantees that the optimization process does not stay forever at any local optimal solution. When the chaotic orbit approaches the global optimal solution, which is the desired UPO, the controller will stabilize it at the UPO, and the optimization process will cease simultaneously. The method has been developed for both discrete-time and continuous-time systems, and validated for some typical chaotic systems such as the Hénon map and the Duffing oscillator, among others.

scholarly journals Best Proximity Point Results in Complex Valued Metric Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Binayak S. Choudhury ◽  
Nikhilesh Metiya ◽  
Pranati Maity
Keyword(s):  
Minimum Distance ◽  
Metric Spaces ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Complex Valued Metric Space ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Global Optimal ◽  
Metric Function ◽  
Complex Valued

We introduce the concept of proximity points for nonself-mappings between two subsets of a complex valued metric space which is a recently introduced extension of metric spaces obtained by allowing the metric function to assume values from the field of complex numbers. We apply this concept to obtain the minimum distance between two subsets of the complex valued metric spaces. We treat the problem as that of finding the global optimal solution of a fixed point equation although the exact solution does not in general exist. We also define and use the concept of P-property in such spaces. Our results are illustrated with examples.

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