  Nowadays, hybrid cloud platforms stand as an attractive solution for
organizations intending to implement combined private and public cloud
applications, in order to meet their profitability requirements. However, this
can only be achieved through the utilization of available resources while
speeding up execution processes. Accordingly, deploying new applications
entails dedicating some of these processes to a private cloud solution, while
allocating others to the public cloud. In this context, the present work is set
to help minimize relevant costs and deliver effective choices for an optimal
service placement solution within minimal execution time. Several evolutionary
algorithms have been applied to solve the service placement problem and are
used when dealing with complex solution spaces to provide an optimal placement
and often produce a short execution time. The standard BPSO algorithm is found
to display a significant disadvantage, namely, of easily trapping into local
optima, in addition to demonstrating a noticeable lack of robustness in dealing
with service placement problems. Hence, to overcome critical shortcomings
associated with the standard BPSO, an Enhanced Binary Particle Swarm
Optimization (E-BPSO) algorithm is proposed, consisting of a modification of
the particle position updating equation, initially inspired from the continuous
PSO. Our proposed E-BPSO algorithm is shown to outperform state-of-the-art
approaches in terms of both cost and execution time, using a real benchmark.
