optimiseSD: (Spatial) optimisation of sampling designs
In sensors4plumes: Test and Optimise Sampling Designs Based on Plume Simulations

Description Usage Arguments Details Value Author(s)

This function optimises a sampling design to achieve minimal cost for a given cost function by applying a given optimisation function. It may take into account fix locations or constrain search to a subset of locations.

optimiseSD(simulations, costFun,
  locationsAll = 1:nLocations(simulations), locationsFix = integer(0),
  locationsInitial = integer(0),
  aimCost = NA, aimNumber = NA,
  optimisationFun,
  nameSave = NA, plot = FALSE, verbatim = FALSE, ...)

`simulations`	`Simulations object`
`costFun`	cost function, must have parameters `simulations` and `locations` (may be prepared by `replaceDefault` with `type = "costFun.optimiseSD"`); it must return cost as a single value or a list where this is the first entry
`locationsAll`	indices of the `locations` that are considered possible sensor locations, by default these are all locations
`locationsFix`	indices of `locations` with fix sensors – to be considered when computing cost, by default this is empty
`locationsInitial`	indices of `locations` where sensors are initially; by default this is empty; if not indicated `aimNumber` random locations may be used, depending on the
`aimCost`	limit value of the `costFun`: the result has to fall below
`aimNumber`	if no `locationsInitial` given, it starts from this number of random locations
`optimisationFun`	`function` that executes an optimisation algorithm; must have parameters `simulations`, `costFun`, `locationsAll`, `locationsFix`, `locationsInitial`, `aimCost`, `aimNumber`, `nameSave` (use `type = "optimisationFun.optimiseSD"` in `replaceDefault`). Output must be a list with `SD` (vector, matrix, or list of one or several SDs) and `cost` (belonging to the SD); it may return other values in `report`
`nameSave`	`character` path and name (without suffix!) where to save intermediate results (sampling design of each iteration)
`plot`	if iteration is plotted (currently disabled)
`verbatim`	print intermediate results and keep sampling designs and cost of all iterations
`...`	further parameters, currently unused

For examples see optimiseSD_genetic, optimiseSD_global, optimiseSD_greedy, optimiseSD_manual,optimiseSD_ssa.

A list

`SD`	`list`, each entry is a matrix of the best (lowest cost) sampling designs found of a size - sampling designs in rows; including `locationsFix`
`evaluation`	`data.frame`, each row belongs to the `SD` of one size, giving the `number` of sensors and the `cost`
`aimSD`	`list` indicating which of the `SD` fulfil the given `aimCost` and `aimNumber`, can be empty if no aims given or aims not reached
`report`	algorithm-specific, e.g. all tested SDs etc., see there.