More recently, another branch of biologically inspired algorithms
have attracted the attention of researchers all over the
world. Algorithms belonging to this field imitate the collective
behavior of a group of social insects (for example, bees, termites,
ants and wasps) to solve complex optimization problems. These
social insects live closely together in their nest and divide up the
various tasks within the colony, such as foraging, nest building and
defense. Each member of the colony performs their own tasks by
interacting or communicating directly or indirectly in their local
environment. Even when one or more individuals fail to carry out
their task, the group as whole can still perform their tasks [19,20].
The process of the division of labor among insects is believed to be
more effective than individual action performed by an individual
insect. These collective and adaptive behaviors of simple insects
have been used by researchers to develop new optimization algorithms,
known as swarm-based optimization algorithms. Particle
swarm optimization [21–24] and ant colony optimization algorithms
[25,26] are well known swarm-based algorithms and are
already employed to solve structural optimization problems.Onthe
hand, bee-inspired algorithms have not yet been employed to solve
structural engineering optimization problems. The main objective
of this paper is to propose a bee-based algorithm for the optimum
design of planer and space trusses consisting of continuous design
variables and to evaluate the performance of the algorithm by comparing
the results of the algorithm with those of other optimization
techniques. Also, modifications that have been made to implement
the algorithm to the structural optimization are described.
The bee-inspired optimization algorithms are based on either a
crude imitation of their mating process or their foraging behaviors.
The algorithms based on the biological process of their
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