Generation of Lifetime-Aware Pareto-Optimal Fronts Using a Stochastic Reliability Simulator

In this paper, two configurations of the S-CO2 Brayton cycles (i.e., the single-recuperated and recompression cycles) are thermodynamically modeled and optimized through a multi-objective approach. Two semi-conflicting objectives, i.e., cycle efficiency (ηc) and cycle specific power (Φsp) are maximized simultaneously to achieve Pareto optimal fronts. The objective of maximum cycle efficiency is to have a smaller and less expensive solar field, and a lower fuel cost in case of a hybrid scheme. On the other hand, the objective of maximum specific power provides a smaller power block, and a lower capital cost associated with recuperators and coolers. The multi-objective optimization is carried out by means of a genetic algorithm which is a robust method for multidimensional, nonlinear system optimization. The optimization process is comprehensive, i.e., all the decision variables including the inlet temperatures and pressures of turbines and compressors, the pinch point temperature differences, and the mass flow fraction of the main compressor are optimized simultaneously. The presented Pareto optimal fronts provide two optimum trade-off curves enabling decision makers to choose their desired compromise between the objectives, and to avoid naive solution points obtained from a single-objective optimization approach. Moreover, the comparison of the Pareto optimal fronts associated with the studied configurations reveals the optimum operational region of the recompression configuration where it presents superior performance over the single-recuperated cycle.

Download Full-text

An Automated Design Methodology of RF Circuits by Using Pareto-Optimal Fronts of EM-Simulated Inductors

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ◽

10.1109/tcad.2016.2564362 ◽

2017 ◽

Vol 36 (1) ◽

pp. 15-26 ◽

Cited By ~ 25

Author(s):

Reinier Gonzalez-Echevarria ◽

Elisenda Roca ◽

Rafael Castro-Lopez ◽

Francisco V. Fernandez ◽

Javier Sieiro ◽

...

Keyword(s):

Design Methodology ◽

Rf Circuits ◽

Automated Design ◽

Pareto Optimal ◽

Pareto Optimal Fronts

Download Full-text

Multiobjective trajectory planner for industrial robots with payload constraints

Robotica ◽

10.1017/s0263574708004359 ◽

2008 ◽

Vol 26 (6) ◽

pp. 753-765 ◽

Cited By ~ 12

Author(s):

R. Saravanan ◽

S. Ramabalan ◽

C. Balamurugan

Keyword(s):

Performance Measures ◽

Industrial Robot ◽

Industrial Robots ◽

Fuzzy Membership Function ◽

Pareto Optimal ◽

Nsga Ii ◽

Multi Objective ◽

Objective Performance ◽

Objective Performance Measures ◽

Pareto Optimal Fronts

SUMMARYA general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of the NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. Both NSGA-II and MODE are best for this problem.

Download Full-text