Academic Coordinator | TBA

Business Coordinator | Manuel Quero García, CEO of Sunntics.

Specialist | Rocío Mingorance, Head of Algorithms and Processes at Sunntics.

Scope | In the context of multi-objective optimization, evolutionary algorithms have proven to be powerful tools due to their ability to explore multiple solutions simultaneously. However, their applicability and effectiveness are not universally standardized. In cases where optimization requires immediate responses, such as the aiming strategy of heliostats at an external tower receiver, evolutionary algorithms may not be the best option due to their high computational cost. For these types of problems, where a balance between accuracy and computing time is sought, approximate or metaheuristic algorithms may be more effective, as they provide approximate solutions in less time. Choosing the optimal algorithm requires a detailed analysis of these, as each method has specific strengths and weaknesses. Factors such as the complexity of the problem to be optimized, the number of objectives, the structure of the search space, and time and computational resource constraints are key considerations in the selection and validation process. A comparative analysis will help identify which algorithms, such as heuristics, metaheuristics, or approximates, achieve a better balance between precision and computational cost, adapting better to the peculiarities of this problem.