Geometric Constraint Solving Using PACOA
Geometric constraint problems can be solved algebraically. So the constraint problem can be transformed to an optimization problem. In this paper introduce a hybrid approach called PACOA in solving geometric constraint problems. The new algorithm possesses large scale search capability of particle swarm optimization (PSO) and the self organized capacity of ant colony optimization (ACO) at the same time. First, we adopt PSO algorithm, it result to produce the initiatory distribution of information elements. Second, we introduce ACO algorithm utilize the pheromone trail laying and following behavior of real ants which use pheromones as a communication medium, to give the precision of the solution. We present and evaluate instantiations of PACOA for solving geometric constraint satisfaction problems. The experiment indicates that the new algorithm accelerates the convergence rate of the algorithm while avoiding the stagnation behavior.
Hua YUAN Wenhui LI Rongqin YI
Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College
国际会议
开封
英文
414-420
2006-10-15（万方平台首次上网日期，不代表论文的发表时间）