Abstract
In this paper, a new algorithm called multi-objective ant-genetic algorithms, which is based on the continuous space optimization is presented to solve constrained multi-objective function optimization problems. For the trait of multi-objective optimization, we define the pheromone instruction inheritance searching strategy and the method of pheromone updating. Then we combine four means of pheromone instruction inheritance searching, introduction of excellent decision-making, decision set updating and changing algorithm termination condition together so that the constringent speed of searching has improved a lot and the quantity of Pareto optimal decisions were controlled, also the distributing area of decisions were enlarged, the diversity of the swarm was maintained. At the same time, the termination conditions of multi-objective ant-genetic algorithms were presented. In the end, an example was listed to prove that the algorithms were effective, and it can find a group of widely distributed Pareto optimal decisions.
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