The ant colony algorithm is an algorithm for finding optimal paths that is based on the behavior of ants searching for food. At first, the ants wander randomly. When an ant finds a source of food, it walks back to the colony leaving "markers" (pheromones) that show the path has food.Ant Colony Optimization targets discrete optimization problems and can be extended to continuous optimization problems which is useful to find approximate solutions.Ant colony algorithm is a heuristic algorithm which is for solving complicated combination optimization.It showed great advantage on solving combinatorial optimization problem since it was proposediops. 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.
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