> ## Documentation Index
> Fetch the complete documentation index at: https://cloudsim-ho-project.puneetchandna.com/llms.txt
> Use this file to discover all available pages before exploring further.

# algorithm_parameters.properties

> The configuration file for the algorithms.

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

## Hippopotamus Optimization (HO)

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

### 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.

### 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.

### 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.

## Baseline Algorithms

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

### First Fit

* `firstfit.sorting.enabled`: Enables sorting of hosts before allocation.

### 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.

### 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.