# Optimize StrathE2E fishing gear activity multipliers to maximumize the likelihood of either observed ecosystem target data or know harvest ratios. Launches a simulated annealing process to find the set of fishing fleet model gear activity multipliers producing the maximum likelihood of either a) observed target data on the state of the ecosystem given specified environmental driving data and ecology model parameters, and effort-harvest ratio scaling parameters, or b) known harvest ratios for each guild in each spatial zone. The former version (optimizing to the state of the ecosystem uses the full StrathE2E system, ie the combined fishing fleet and ecology models. However, the latter version, i.e. optimizing to known harvest ratios, uses only the fishing fleet model. The version of optimization procedure is selected by an argument in the function call. ```r e2e_optimize_act( model, selection = "HR", n_iter = 3000, start_temperature = 0.5, cooling = 0.985, csv.output = FALSE, runtime.plot = TRUE, n_traj = 100, deltaHi = 0.2, attenuationstep = 500, deltaG = 0.25, nyears = 40, quiet = TRUE ) ``` ## Arguments - `model`: R-list object generated by the e2e_read() function which defined the model configuration. - `selection`: Text string from a list to select the method for optimization. Select from: "HR", "ECO", Remember to include the phrase within "" quotes (default = "HR"). "HR" selects a scheme for optimizing the activity multipler to known harvest ratios and uses just the fishing fleet model. "ECO" selects a scheme for optimizing to the observational data in the state of the ecosystem and so uses both the fishing fleet and ecology models. - `n_iter`: Number of iterations of the model (per trajectory in the case of selection = "HR") (default=3000). Suggest a value of 500 if selection = "ECO". - `start_temperature`: Initial value of the simulated annealing temperature parameter (default=1). Suggested a value of 0.5 if selection = "ECO". Values in the range 0.0005 - 5 are reasonable. Higher values increase the probability of rejecting parameter combinations producing an improvement in likelihood. - `cooling`: Rate at which the simulated annealing temperature declines with iterations (default=0.985). Suggested a value of 0.975 if selection = "ECO". - `csv.output`: Logical. If TRUE then enable writing of csv output files (default=FALSE). - `runtime.plot`: Logical. If FALSE then disable runtime plotting of the progress of the run - useful for testing (default=TRUE) - `n_traj`: Specific to selection="HR" : Number of repeats (trajectories) of the simulated annealing process (default=100). - `deltaHi`: Specific to selection="HR" : Initial coefficient of variation for jiggling the activity multiplier values (default=0.2). This is set in the parameter file "optimize_fishing.csv" in the case where selection="ECO" and can be manually varied during a run. - `attenuationstep`: Specific to selection="HR" : Number of itereations between down-steps of the jiggling factor applied to the multiplier values. The jiggling rate is attenuated by a factor of 2 every xx iterations (default=500). - `deltaG`: Specific to selection="HR" : Coefficient of variation for jiggling the gear linkage values (default=0.25). - `nyears`: Specific to selection="ECO" : Number of years to run the ecology model in each iteration (default=40). - `quiet`: Logical. Specific to selection="ECO" : If TRUE then suppress informational messages at the start of each iteration (default=TRUE). ## Returns A list object containing the data from each trajectory and the final accepted parameter values. Optionally (by default), csv files of the histories and the final accepted parameter values. The latter are returned to the model parameter folder in a format to be read back into the model. ## Details Generic details for both optimization procedures Simulated annealing is is a probabilistic technique for approximating the global optimum of a given function. As implemented here the process searches the parameter space of a model to locate the combination which maximises the likelihood of a set of observed data corresponding to a suite of derived outputs. Parameter combinations which result in an improved likelihood may be rejected according to a probability ('temperature') which decreases as the iterations progress. This is to avoid becoming stuck at local likelihood-maxima. The rate at which the 'temperature' decreases is set by a 'cooling' parameter (fraction of previous temperature at each iteration, 0

e2e_optimize_act function