• Energy Research

ABC-X is a generalized, automatically configurable framework for the Artificial Bee Colony (ABC) metaheuristic. ABC-X has recently been proposed and it has initially been tested on different benchmark functions sets, showing very good results when compared to known ABC algorithms. However, it has never been used in an industrial application. In this case study on a real-world problem, the performance of ABC-X for the economic power dispatch problem (EPDP) with valve point effects is investigated. The algorithm's performance is tested on four instances of different size and compared with the performance of other algorithms that have been proposed earlier for this problem. The results obtained show that ABC-X can successfully solve the EPDP with valve point effects.

The introduction of ant colony optimization (ACO) and to survey its most notable applications are discussed. Ant colony optimization takes inspiration from the forging behavior of some ant species. These ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of ants is the main source of inspiration for the development of ant colony optimization. In ACO a number of artificial ants build solutions to an optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich optimization problems that include stochasticity.