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Ant colony systems for optimization problems in dynamic environments

Ant colony systems for optimization problems in dynamic environments

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Ant colony optimization (ACO) is an intelligent bionic algorithm which simulates the foraging behavior of ant colony. The conventional ACOs mainly deal with the static optimization problems. In other words, the environment of problem maintains invariant. Actually, the most problems in reality are dynamic, namely, the changing environments. The ACO can use its robustness and self-adaptability to resolve dynamic problems properly. In this chapter, the ACO with neighborhood search is introduced to address dynamic traveling salesman problem and the ACO with improved K-means clustering algorithm, which uses three immigrants schemes including random immigrants, elitism-based immigrants and memory-based immigrants, is used for dynamic location routing problem. Several conventional ACOs and other heuristic algorithms are utilized to compare with new ACOs in the corresponding dynamic problems. The comparative experiments demonstrate two novel ACOs are effective and efficient for respective dynamic optimization problems.

Chapter Contents:

  • Abstract
  • 4.1 Introduction
  • 4.2 Dynamic optimization problems
  • 4.2.1 Dynamic traveling salesman problem
  • 4.2.1.1 Stationary TSP
  • 4.2.1.2 Dynamic TSP
  • 4.2.2 Dynamic location routing problem
  • 4.2.2.1 Conventional LRP
  • 4.2.2.2 Dynamic LRP
  • 4.3 Ant colony optimization
  • 4.3.1 Ant system
  • 4.3.2 Elitist ant system
  • 4.3.3 MAX–MIN ant system
  • 4.3.4 Ant colony system
  • 4.3.5 Population based ACO
  • 4.3.6 ACO with neighborhood search
  • 4.3.7 Clustering ACO with immigrants schemes
  • 4.3.7.1 Clustering algorithm
  • 4.3.7.2 Three immigrants schemes
  • 4.3.7.3 KACO with immigrants schemes
  • 4.4 Experiments
  • 4.4.1 The experiment for DTSP
  • 4.4.1.1 Experimental settings
  • 4.4.1.2 Experimental analysis
  • 4.4.2 The experiment for DLRP
  • 4.4.2.1 Experimental settings
  • 4.4.2.2 Experimental analysis
  • 4.5 Conclusions
  • References

Inspec keywords: search problems; pattern clustering; ant colony optimisation; travelling salesman problems

Other keywords: ACO; dynamic location routing problem; elitism-based immigrants; ant colony optimization; ant colony systems; dynamic traveling salesman problem; intelligent bionic algorithm; static optimization problems

Subjects: Combinatorial mathematics; Optimisation techniques; Combinatorial mathematics; Optimisation techniques

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