One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adapti...One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research paper.The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima problem.Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems.An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper.The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE algorithm.Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE algorithms.This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.展开更多
Focused on several problems during robot target tracking, and proposed a new kind of scheme and algorithm for it. The hybrid systematic structure reduces the control complexity and guarantees the tracking effectivenes...Focused on several problems during robot target tracking, and proposed a new kind of scheme and algorithm for it. The hybrid systematic structure reduces the control complexity and guarantees the tracking effectiveness as well as the control stability. The convergence and the feasibility of the algorithm are analyzed and proofed thoroughly. An on-line updating method for navigation coefficient is presented. Finally, the control scheme and proposed algorithm is applied to the real robotic system. The simulation and experimental results show its effectiveness.展开更多
基金This work was supported by the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,which funded this research work through project number 959.
文摘One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research paper.The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima problem.Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems.An adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this paper.The proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE algorithm.Experimental results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE algorithms.This algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.
文摘Focused on several problems during robot target tracking, and proposed a new kind of scheme and algorithm for it. The hybrid systematic structure reduces the control complexity and guarantees the tracking effectiveness as well as the control stability. The convergence and the feasibility of the algorithm are analyzed and proofed thoroughly. An on-line updating method for navigation coefficient is presented. Finally, the control scheme and proposed algorithm is applied to the real robotic system. The simulation and experimental results show its effectiveness.
