Simple local search problems that are hard to solve
SIAM Journal on Computing
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
New ideas in optimization
Future Generation Computer Systems
An Heuristic Method for GPS Surveying Problem
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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Ant Colony Optimization(ACO) have been used successfully to solve hard combinatorial optimization problems. This metaheuristics method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes between their colonies to feeding sources and back. In this paper ACO algorithms are developed to provide near-optimal solutions for Global Positioning System surveying problem. In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed. Minimizing the cost of this schedule is the goal of this work.