A number of modern metaheuristic optimization techniques are being exploited to work out a single-objective economic dispatch (ED) problem. The dispatch problems even become more complicated and complex when they consider operational and system constraints, such as network transmission losses, valve-point loading effects originating due to sequential opening of a number of steam admission valves to meet the ever-increasing demand, ramp rate limits, prohibited operating zones, multiple fuel options, spinning reserve, and so on. The heavy constraints make the otherwise convex linear smooth dispatch problem as highly nonconvex nonlinear nonsmooth one. Finding optimal solution for such kind of a constrained nonlinear problem through the deterministic numerical and convex characteristics-based optimization techniques is a difficult task to accomplish. Researchers have frequently employed one of the metaheuristic optimization techniques with powerful computational ability named particle swarm optimization (PSO) to deal with this rather a complicated and toilsome dispatch problem. In Part I of the two-part paper, a comprehensive review or a survey of PSO and its modified versions (involve alterations in the basic structure of PSO) to resolve the constrained ED problem is presented. Part II covers purely the survey of hybrid forms of PSO (hybridization of PSO with other optimization techniques) to tackle the ED problem. The survey is presented in such a way that readers may understand how PSO can be made computationally more efficient.