  This work presents a comparative evaluation of four population-based
optimization algorithms for workflow scheduling in cloud-fog environments.
These algorithms are as follows: Particle Swarm Optimization (PSO), Genetic
Algorithm (GA), Differential Evolution (DE) and GA-PSO. This work also provides
the motivational groundwork for the weighted sum objective function for the
workflow scheduling problem and develops this function based on three
objectives: makespan, cost and energy. The recently proposed FogWorkflowSim is
used as the simulation environment with the aforementioned objectives serving
performance metrics. Results show that hybrid combination of the GA-PSO
algorithm exhibits slightly better than the standard algorithms. Future work
will include expansion of the workflows used by increasing the number of tasks
as well as adding some more workflows. The addition of some more objectives to
the weighted objective function will also be pursued
