Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design

By A. Toffolo, A. Lazzaretto. 2002

248 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Exergoeconomic analysis and optimization of combined heat and power production: A review (2009, methodology): “This method has been applied and compared with SPECO approach for the exergoeconomic analysis and optimization of combined heat and power plants [45–57,60].”
Thermo‐economic‐environmental multiobjective optimization of a gas turbine power plant with preheater using evolutionary algorithm (2011, methodology): “This code, which is developed based on GA, has been applied to find the set of Pareto optimal solution [8] with respect to aforementioned objective functions.”
A modified shuffle frog leaping algorithm for multi-objective optimal power flow (2011, methodology): “Some of these algorithms that have been applied to solve multi-objective problems are stochastic search techniques (MOSST) [33], multiobjective evolutionary algorithm (MOEA) [34], Niched Pareto genetic algorithm (NPGA) [35], strength Pareto evolutionary algorithm (SPEA) [36], non-dominating sorting ”
Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms (2012, background): “Several other investigators (Sahoo 2008 , Toffolo and Lazzaretto 2002 , Valdes et al .”