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Overview of metaheuristic optimization

Overview of metaheuristic optimization

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Metaheuristic optimization methods have become significant tools and often the only way of solving practical optimization problems. The basic requirement from these methods is to obtain the global solution or a solution close to the global optimum in a reasonable time.

Chapter Contents:

  • 1.1 Introduction
  • 1.2 Description of metaheuristics
  • 1.3 Principle of population-based metaheuristics
  • 1.3.1 Genetic algorithm
  • 1.3.2 Differential evolution
  • 1.3.3 Evolutionary programing
  • 1.3.4 Backtracking search optimization algorithm
  • 1.3.5 Particle swarm optimization
  • 1.3.6 Ant colony optimization
  • 1.3.7 Artificial bee colony
  • 1.3.8 Gravitational search algorithm
  • 1.3.9 Wind-driven optimization
  • 1.3.10 Colliding bodies optimization
  • 1.3.11 Black hole algorithm
  • 1.3.12 Gray wolf optimizer
  • 1.3.13 Firefly algorithm
  • 1.3.14 Cuckoo search algorithm
  • 1.3.15 Moth swarm algorithm
  • 1.3.16 Krill herd algorithm
  • 1.3.17 Shuffled frog-leaping algorithm
  • 1.3.18 Bacterial colony foraging optimization
  • 1.3.19 Biogeography-based optimization
  • 1.3.20 Teaching–learning-based optimization
  • 1.3.21 League championship algorithm
  • 1.3.22 Mine blast algorithm
  • 1.3.23 Sine cosine algorithm
  • 1.3.24 Harmony search
  • 1.3.25 Imperialist competitive algorithm
  • 1.3.26 Differential search algorithm
  • 1.3.27 Glowworm swarm optimization
  • 1.3.28 Spiral optimization algorithm
  • 1.3.29 The Jaya algorithm
  • 1.3.30 Creating a ''new''algorithm
  • 1.4 Criticism of metaheuristics
  • 1.5 Educational software—metahopt
  • 1.6 Conclusion
  • References

Inspec keywords: optimisation

Other keywords: global optimum; optimization problems; metaheuristic optimization method

Subjects: Optimisation techniques; Optimisation; Optimisation techniques

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