A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic network programming with rule accumulation considering judgment order
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Genetic Network Programming (GNP), one of the extended evolutionary algorithms was proposed, whose gene is constructed by the directed graph. GNP is distinguished from other evolutionary techniques in terms of its compact structure and implicit memory function. GNP can perform a global searching, but it lacks of the exploitation ability. Since the behavior of GNP is characterized by the balance between exploitation and exploration in the search space, we proposed a hybrid algorithm in this paper that combines GNP with Ant Colony Optimization (ACO). The genetic operators are operated using the pheromone information in some special generations. We applied the proposed hybrid algorithm to a complicated real world problem, that is , Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.