Abstract
Ant colony algorithm is a heuristic algorithm which is　fit for solving complicated combination optimization.It showed great advantage on solving combinatorial optimization problem since it was proposed. The algorithm uses distributed parallel computing and positive feedback mechanism, and is easy to combine with other algorithms.This ant colony algorithm has already been widespread used in the field of discrete space optimization, however, is has been rarely used for continuous space optimization question.On the basis of basic ant colony algorithm principles and mathematical model, this paper proposes an ant colony algorithm for solving continuous space optimization question.Comparing with the ant colony algorithm, the new algorithm improves the algorithm in aspects of ant colony initialization, information density function, distribution algorithms, direction of ant colonymotion, and so on. The new algorithm uses multiple optimization strategy, such as polynomial time reduction and branching factor, and improves the ant colony algorithm effectively.
Highlights
Using for aging behaviour of ant colony as inspiration, Italian scholar Marco Dorigo and Vittorio Maniezzo designed the first ACO algorithm: Ant System (AS), which is a highly innovative meta-heuristic algorithm
Since the ant colony algorithm has developed for many years, the researches of ant colony algorithm have been extended from single TSP field to a lot of fields of application
The ant colony algorithm has developed form one-dimensional static optimization to multidimensional dynamic combinatorial optimization, from research in discrete domain to research in continuous domains
Summary
Using for aging behaviour of ant colony as inspiration, Italian scholar Marco Dorigo and Vittorio Maniezzo designed the first ACO algorithm: Ant System (AS), which is a highly innovative meta-heuristic algorithm. The ant colony algorithm has developed form one-dimensional static optimization to multidimensional dynamic combinatorial optimization, from research in discrete domain to research in continuous domains. The great advance of research of ant colony algorithm makes a widespread application, this paper will optimize traditional ant colony algorithm for characteristic of continuous spaces in aspects of ant colony initialization, information density function, distribution algorithms, ant colony direction of motion, and so on.This paper will improve the ant colony algorithm by using optimization strategy of polynomial time reduction, branching factor, etc
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