Abstract
As a swarm intelligent technique, particle swam optimization (PSO) is inherently an evolutionary algorithm that simulates the animal collective behaviors. Since each individual has a different experience based on its position, each particle produces its own movement based on these experiences. The original PSO lacks deeper interaction between individuals at a local level. This implementation of PSO will explore the influence that individuals in its neighborhood have on its search and how it can improve on the capabilities of PSO. Therefore, in this study, a variation will be added to the current PSO technique and applied to Economic Dispatch (ED) problem. ED is an integral component in planning and operation of power systems, because it results in the most cost-efficient method of power delivery while serving the demand. In solving ED there are also other considerations such as power loss that play a large role in cost. More recently, evolutionary algorithms have replaced conventional methods of solving these power system problems. They require less computation and are also very useful in large non-linear problems.