Design of experiment for tuning parameters of an ant colony optimization method for the constrained shortest Hamiltonian path problem in the grid networks

Abstract

In a grid network, the nodes could be traversed either horizontally or vertically. The constrained shortest Hamiltonian path goes over the nodes between a source node and a destination node, and it is constrained to traverse some nodes at least once while others could be traversed several times. There are various applications of the problem, especially in routing problems. It is an NP-complete problem, and the well-known Bellman-Held-Karp algorithm could solve the shortest Hamiltonian circuit problem within O(2 n n 2 ) time complexity; however, the shortest Hamiltonian path problem is more complicated. So, a metaheuristic algorithm based on ant colony optimization is applied to obtain the optimal solution. The proposed method applies the rooted shortest path tree structure since in the optimal solution the paths between the restricted nodes are the shortest paths. Then, the shortest path tree is obtained by at most O(n 3 ) time complexity at any iteration and the ants begin to improve the solution and the optimal solution is constructed in a reasonable time. The algorithm is verified by some numerical examples and the ant colony parameters are tuned by design of experiment method, and the optimal setting for different size of networks are determined.

Multiobjective Optimization Management of Construction Engineering Based on Ant Colony Algorithm

“…Among them, the dynamic weight method has the following advantages compared with the other two methods: first, the weight coefficient is easy to operate and can quickly and reasonably transform the multiobjective optimization problem into a single-objective optimization problem; secondly, the weight coefficient changes dynamically, which avoids falling into local optimization in the solution process and improves the scientificity of the optimization result. In addition, this method is mature and has certain reference experience and can achieve good coordination with computer language algorithm [12]. erefore, this paper selects the dynamic weight method to solve the multiobjective optimization problem.…”

Design and Application of a Resource Allocation Method for CAEVs Internet of Things Based on Swarm Intelligence Computing

“…(2) Improved pheromone updating formula Upon shifting the focus of the ACA from solving the shortest distance path to solving the shortest delay path, it becomes necessary to update the pheromone formulas [25]. These formulas play a crucial role in guiding the ants' search for optimal paths.…”