[1]CHEN Mingjie,HUANG Baichuan,ZHANG Min.Function optimization based on an improved hybrid ACO[J].CAAI Transactions on Intelligent Systems,2012,7(4):370-376.
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Function optimization based on an improved hybrid ACO

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 4 Page number: 370-376 Column: 学术论文—人工智能基础 Public date: 2012-08-25
Title:
Function optimization based on an improved hybrid ACO
Author(s):
CHEN Mingjie;  HUANG Baichuan;  ZHANG Min
College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
improved hybrid ant colony optimization;  function optimization;  selfadaptive;  Gaussian mutation; ant colony optimization
CLC:
TP181
DOI:
-
Abstract:
Considering the low evolution speed and the tendency towards stagnation of ant colony optimization (ACO), an improved ACO was discussed based on an adaptive pheromone evaporation factor. To avoid the defect of ACO easily falling into the local optimum, another improved ACO was proposed based on Gaussian variation of decision variables. To overcome the shortcoming of the slow speed of ACO, a new and improved ACO was given based on boundary selftuning of the decision variables. Finally, an improved hybrid ant colony algorithm was proposed, which combined the adaptive pheromone evaporation factor, Gaussian variation of decision variables, and boundary selftuning of the decision variables. When applied to function optimization, the simulation results show that the improved hybrid ACO has a higher degree of accuracy, a higher convergence ratio, and improved optimization performance.
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