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This file contains the specific parameters for the Hippopotamus Optimization (HO) algorithm and the baseline algorithms.

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Hippopotamus Optimization (HO)

The HO algorithm has a rich set of parameters that allow you to fine-tune its behavior.

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Core Parameters

  • ho.population.size: The number of “hippos” (solutions) in the population.
  • ho.max.iterations: The maximum number of iterations the algorithm will run for.

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Movement Strategy

These parameters control how the hippos move in the search space.
  • ho.exploration.rate.initial and ho.exploration.rate.final: Control the balance between exploration and exploitation.
  • ho.levy.flight.enabled: Enables Levy flight, a random walk strategy that can help escape local optima.

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Multi-objective Optimization

These parameters control how the algorithm handles multiple objectives (e.g., resource utilization, power consumption).
  • ho.pareto.enabled: Enables Pareto optimization, a technique for finding a set of optimal solutions.
  • ho.penalty.sla.violation and ho.penalty.allocation.failure: Penalties for violating service level agreements or failing to allocate a VM.

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Baseline Algorithms

These are the parameters for the baseline algorithms that are used for comparison.

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First Fit

  • firstfit.sorting.enabled: Enables sorting of hosts before allocation.

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Best Fit

  • bestfit.waste.calculation: The method used to calculate the waste when placing a VM.
  • bestfit.cpu.weight and bestfit.ram.weight: The weights for CPU and RAM in the waste calculation.

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Genetic Algorithm (GA)

  • ga.population.size: The size of the population in the GA.
  • ga.max.generations: The maximum number of generations for the GA.
  • ga.mutation.rate and ga.crossover.rate: The rates for mutation and crossover, which are key genetic operators.