This
paper
presents
two
simple
yet
powerful
optimization
algorithms
named
Best-Mean-Random
(BMR)
and
Best-Worst-Randam
(BWR)
to
handle
both
constrained
unconstrained
problems.
These
are
free
of
metaphors
algorithm-specific
parameters.
The
BMR
algorithm
is
based
on
the
best,
mean,
random
solutions
population
generated
for
solving
a
given
problem;
BWR
worst,
solutions.
performances
proposed
investigated
12
engineering
problems
results
compared
with
very
recent
(in
some
cases
more
than
30
algorithms).
Furthermore,
computational
experiments
conducted
standard
benchmark
including
5
recently
developed
having
distinct
characteristics.
proved
better
competitiveness
superiority
algorithms.
research
community
may
gain
an
advantage
by
adapting
these
solve
various
real-life
across
scientific
disciplines.
This
paper
presents
two
simple
yet
powerful
optimization
algorithms
named
Best-Mean-Random
(BMR)
and
Best-Worst-Randam
(BWR)
to
handle
both
constrained
unconstrained
problems.
These
are
free
of
metaphors
algorithm-specific
parameters.
The
BMR
algorithm
is
based
on
the
best,
mean,
random
solutions
population
generated
for
solving
a
given
problem;
BWR
worst,
solutions.
performances
proposed
investigated
12
engineering
problems
results
compared
with
very
recent
(in
some
cases
more
than
30
algorithms).
Furthermore,
computational
experiments
conducted
standard
benchmark
including
5
recently
developed
having
distinct
characteristics.
proved
better
competitiveness
superiority
algorithms.
research
community
may
gain
an
advantage
by
adapting
these
solve
various
real-life
across
scientific
disciplines.
