These functions generate option lists (and provide defaults) for the SOMA algorithm variants available in the package, which control how the algorithm will proceed and when it will terminate. Each function corresponds to a different top-level strategy, described in a different reference.
all2one(
populationSize = 10L,
nMigrations = 20L,
pathLength = 3,
stepLength = 0.11,
perturbationChance = 0.1,
minAbsoluteSep = 0,
minRelativeSep = 0.001
)t3a(
populationSize = 30L,
nMigrations = 20L,
nSteps = 45L,
migrantPoolSize = 10L,
leaderPoolSize = 10L,
nMigrants = 4L,
minAbsoluteSep = 0,
minRelativeSep = 0.001
)
pareto(
populationSize = 100L,
nMigrations = 20L,
nSteps = 10L,
perturbationFrequency = 1,
stepFrequency = 1,
minAbsoluteSep = 0,
minRelativeSep = 0.001
)
The number of individuals in the population. It is recommended that this be somewhat larger than the number of parameters being optimised over, and it should not be less than 2. The default varies by strategy.
The maximum number of migrations to complete.
The distance towards the leader that individuals may migrate. A value of 1 corresponds to the leader's position itself, and values greater than one (recommended) allow for some overshoot.
The granularity at which potential steps are evaluated.
It is recommended that the pathLength not be a whole multiple of
this value.
The probability that individual parameters are changed on any given step.
The smallest absolute difference between the maximum and minimum cost function values. If the difference falls below this minimum, the algorithm will terminate. The default is 0, meaning that this termination criterion will never be met.
The smallest relative difference between the maximum and minimum cost function values. If the difference falls below this minimum, the algorithm will terminate.
The number of candidate steps towards the leader per migrating
individual. This option is used instead of pathLength and
stepLength under the T3A and Pareto strategies, where the step
length is variable.
The number of randomly selected individuals to include in the migrant and leader pools, respectively, under the T3A strategy.
The number of individuals that will migrate, at each migration, under the T3A strategy.
Scale factors affecting how rapidly the perturbation probability and step sizes fluctuate under the Pareto strategy.
A list of class "soma.options".
All To One (the all2one function) is the original SOMA strategy. At
each ``migration'', the cost function is evaluated for all individuals in
the population, and the one with the lowest value is designated the
``leader''. All other individuals migrate towards the leader's position in
some or all dimensions of the parameter space, with a fixed probability of
perturbation in each dimension. Each migration is evaluated against the cost
function at several points on the line towards the leader, and the location
with the lowest value becomes the individual's starting position for the
next migration.
The Team To Team Adaptive (T3A) strategy (Diep, 2019) differs in that only a random subset of individuals are selected into a migrant pool and a leader pool for any given migration. A subset of most optimal migrants are then migrated towards the single most optimal individual from the leader pool. The perturbation probability and step length along the trajectory towards the leader also vary according to formulae given by the strategy author as the algorithm progresses through the migrations.
In the Pareto strategy (Diep et al., 2019), all individuals are sorted by cost function value at the start of each migration. The leader is selected randomly from the top 4% (20% of 20%) of most optimal individuals, and a single migrant is chosen at random from between the 20th and the 36th percentiles of the population (the top 20% of the bottom 80%). The perturbation probability and the step length again vary across migrations, but this time in a sinusoidal fashion, and the migrant is updated in all dimensions, but some more slowly than others.
I. Zelinka (2004). SOMA - self-organizing migrating algorithm. In G.C. Onwubolu & B.V. Babu, eds, New optimization techniques in engineering. Volume 141 of ``Studies in Fuzziness and Soft Computing'', pp. 167-217. Springer.
Q.B. Diep (2019). Self-Organizing Migrating Algorithm Team To Team Adaptive <U+2013> SOMA T3A. In proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1182-1187. IEEE.
Q.B. Diep, I. Zelinka & S. Das (2019). Pareto-Based Self-Organizing Migrating Algorithm. Mendel 25(1):111-120.