Double-deck elevator system using genetic network programming with genetic operators based on pheromone information

ABSTRACT

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.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

General Terms:
Algorithms, Performance

Keywords:
ant colony optimization, elevator group supervisory control system, genetic network programming, genetic operator, hybrid algorithm