4 research outputs found
Metaheuristics for Transmission Network Expansion Planning
This chapter presents the characteristics of the metaheuristic algorithms used to solve the transmission network expansion planning (TNEP) problem. The algorithms used to handle single or multiple objectives are discussed on the basis of selected literature contributions. Besides the main objective given by the costs of the transmission system infrastructure, various other objectives are taken into account, representing generation, demand, reliability and environmental aspects. In the single-objective case, many metaheuristics have been proposed, in general without making strong comparisons with other solution methods and without providing superior results with respect to classical mathematical programming. In the multi-objective case, there is a better convenience of using metaheuristics able to handle conflicting objectives, in particular with a Pareto front-based approach. In all cases, improvements are still expected in the definition of benchmark functions, benchmark networks and robust comparison criteria
Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'
In the power and energy systems area, a progressive increase of literature
contributions containing applications of metaheuristic algorithms is occurring.
In many cases, these applications are merely aimed at proposing the testing of
an existing metaheuristic algorithm on a specific problem, claiming that the
proposed method is better than other methods based on weak comparisons. This
'rush to heuristics' does not happen in the evolutionary computation domain,
where the rules for setting up rigorous comparisons are stricter, but are
typical of the domains of application of the metaheuristics. This paper
considers the applications to power and energy systems, and aims at providing a
comprehensive view of the main issues concerning the use of metaheuristics for
global optimization problems. A set of underlying principles that characterize
the metaheuristic algorithms is presented. The customization of metaheuristic
algorithms to fit the constraints of specific problems is discussed. Some
weaknesses and pitfalls found in literature contributions are identified, and
specific guidelines are provided on how to prepare sound contributions on the
application of metaheuristic algorithms to specific problems