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Evolutionary algorithms

Evolutionary algorithms

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Emerging Evolutionary Algorithms for Antennas and Wireless Communications — Recommend this title to your library

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Swarm intelligence (Si) algorithms are special category of EAs. The essential concept of SI is the collective behavior of decentralized and self-organized swarms. There are several popular SI algorithm families that among others include particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC). The popularity of SI algorithms is due to the fact that they in general can handle efficiently arbitrary optimization problems. Additionally, SI algorithms as it can be found from the literature have been widely utilized to solve several problems in antennas and in wireless communications. The swarm behavior of bird flocking and fish schooling is modeled mathemati-cally by the PSO algorithm. One may find several PSO variants in the literature. The most frequently applied PSO variants include the inertia weight PSO (IWPSO) and the constriction factor PSO (CFPSO). Moreover, comprehensive learning particle swarm optimizer (CLPSO) is a PSO algorithm that has been applied to antenna design problems. The PSO algorithm is intrinsically suitable for applica-tion to real-valued problems. Thus, binary PSO (BPSO) versions should be used for solving discrete-valued problems. BPSO is one of the most popular discrete PSO algorithms. BPSO maps real values to the discrete set by using a sigmoid trans-fer function. Additionally, several new transfer functions that perform better than the original algorithm have been introduced by the authors. Furthermore, Boolean PSO is another BPSO version with main characteristic the usage of binary oper-ators for velocity and position update. Several authors have applied Boolean PSO to antenna design problems.

Chapter Contents:

  • 2.1 Swarm intelligence algorithms
  • 2.1.1 Initialization
  • 2.1.2 Inertia weight particle swarm optimization
  • 2.1.3 Constriction factor particle swarm optimization
  • 2.1.4 Comprehensive learning particle swarm optimizer
  • 2.1.5 PSO for discrete-valued problems
  • 2.1.5.1 Binary PSO variants
  • 2.1.5.2 Boolean PSO
  • 2.1.6 Artificial bee colony algorithm
  • 2.1.6.1 Gbest-guidedABC
  • 2.1.7 Ant colony optimization
  • 2.1.8 Emerging nature-inspired swarm algorithms
  • 2.1.8.1 Grey wolf optimizer
  • 2.1.8.2 Binary GWO versions
  • 2.1.8.3 Whale optimization algorithm
  • 2.1.8.4 Salp swarm algorithm
  • 2.2 Differential evolution
  • 2.2.1 Self-adaptive DE algorithms
  • 2.2.1.1 jDE algorithm
  • 2.2.1.2 Barebones DE
  • 2.2.1.3 Composite DE
  • 2.2.1.4 CoDE with eigenvector-based crossover operator (CoDE-EIG)
  • 2.2.1.5 The SaDE algorithm
  • 2.2.1.6 The JADE algorithm
  • 2.2.2 Novel binary differential evolution
  • 2.3 Biogeography-based optimization
  • 2.3.1 Chaotic BBO
  • 2.4 Emerging evolutionary algorithms
  • 2.4.1 Biology-based algorithms
  • 2.4.1.1 Firefly algorithm
  • 2.4.1.2 Monarch butterfly optimization
  • 2.4.1.3 Greedy strategy and self-adaptive crossover MBO (GCMBO)
  • 2.4.1.4 Moth search algorithm
  • 2.4.1.5 Elephant herding optimization
  • 2.4.1.6 Shuffled frog-leaping algorithm
  • 2.4.2 Physics-based algorithms
  • 2.4.2.1 Gravitational search algorithm
  • 2.4.2.2 PSOGSA
  • 2.4.2.3 Wind-driven optimization
  • 2.4.3 Human social behavior-based algorithms
  • 2.4.3.1 Teaching–learning-based optimization
  • 2.4.3.2 Jaya
  • 2.4.3.3 TLBO–Jaya algorithm
  • 2.4.4 Music-based algorithms
  • 2.4.4.1 Harmony search algorithm
  • 2.5 Opposition-based learning
  • 2.5.1 OBL types
  • 2.5.2 OBL algorithm description
  • 2.5.3 Modified generalized OBBO
  • 2.6 Multi-objective algorithms
  • 2.6.1 Non-dominated sorting genetic Algorithm-II
  • 2.6.1.1 Non-dominated ranking
  • 2.6.1.2 Algorithm description
  • 2.6.2 Non-dominated sorting genetic Algorithm-III
  • 2.6.3 Generalized differential evolution
  • 2.6.4 Speed-constrained multi-objective PSO
  • 2.6.5 Multi-objective BBO
  • 2.6.6 Computational complexity of MO algorithms
  • References

Inspec keywords: ant colony optimisation; optimisation; evolutionary computation; artificial bee colony algorithm; antennas

Other keywords: SI algorithms; IWPSO; emerging evolutionary algorithms; wireless communications; PSO algorithm; Boolean PSO; fish schooling; EA special category; particle swarm optimization; sigmoid transfer function; artificial bee colony; several popular SI algorithm; comprehensive learning particle swarm optimizer; ant colony optimization; BPSO maps; self-organized swarms; bird flocking; antenna design problem; inertia weight PSO; binary PSO

Subjects: Optimisation techniques; Single antennas; Optimisation techniques

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