
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>
Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

handle: 11583/2847123
In the power and energy systems area, a progressive increase of literature contributions that contain 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 that are 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 that concern 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 that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.
FOS: Computer and information sciences, metaheuristics, Technology, convergence, Computer Science - Artificial Intelligence, T, Computer Science - Neural and Evolutionary Computing, underlying principles, Artificial Intelligence (cs.AI), evolutionary computation, Neural and Evolutionary Computing (cs.NE), large-scale optimization, constraints, large-scale optimization; metaheuristics; underlying principles; constraints; convergence; evolutionary computation; global optimum; guidelines; review; survey
FOS: Computer and information sciences, metaheuristics, Technology, convergence, Computer Science - Artificial Intelligence, T, Computer Science - Neural and Evolutionary Computing, underlying principles, Artificial Intelligence (cs.AI), evolutionary computation, Neural and Evolutionary Computing (cs.NE), large-scale optimization, constraints, large-scale optimization; metaheuristics; underlying principles; constraints; convergence; evolutionary computation; global optimum; guidelines; review; survey
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).35 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
