Predator-prey optimization with heterogeneous swarms

Predator-prey optimization with heterogeneous swarms

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Predator-prey optimization algorithms use the interaction between predator and prey particles to control the balance between local and global search in particle swarm optimization. Since their introduction in 2002, predator-prey optimizers have been successfully applied to many practical problems, frequently outperforming other particle swarm algorithms. In this chapter, we will start by presenting the original predator-prey optimizing algorithm and to review some of its applications. We will then describe the most recent version of the algorithm, the scouting predator- prey optimizer, where scout particles are proposed as a mechanism to introduce new exploratory behaviors in the new heterogeneous swarm. Scout particles can be used to improve the predator-prey algorithm in different ways, from integrating previous knowledge to increase performance in specific problems to introducing new heuristics that globally improve the algorithm. We illustrate the effect of using different scout particles by empirically comparing the performance of several variants of the scouting predator-prey optimizer on a large set of benchmark problems, carefully chosen to present the algorithms with different challenges. Finally, the scouting predator-prey algorithm will be compared with several particle swarm optimizers and differential evolution algorithms to investigate how competitive the algorithm is with state-ofthe-art particle swarm and evolutionary optimizers.

Chapter Contents:

  • Abstract
  • 5.1 Introduction
  • 5.2 The algorithms
  • 5.2.1 The standard particle swarm optimizer
  • 5.2.2 The scouting predator–prey optimizer
  • The predator–prey mechanism
  • Scout particles
  • 5.3 Experimental setup
  • 5.3.1 Benchmark algorithms
  • 5.4 Experimental results
  • 5.4.1 Global results
  • 5.4.2 Intermediate results
  • 5.4.3 Results for the knowledge- based scout particle
  • 5.5 Conclusions
  • References

Inspec keywords: evolutionary computation; particle swarm optimisation; predator-prey systems

Other keywords: particle swarm algorithms; original predator-prey optimizing algorithm; particle swarm optimization; scouting predator-prey optimizer; predator-prey optimization algorithms; evolutionary optimizers; predator-prey optimizers; scouting predator-prey algorithm; heterogeneous swarm; particle swarm optimizers; differential evolution algorithms

Subjects: Systems theory applications in biology and medicine; Optimisation techniques; Systems theory applications in natural resources and ecology

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