A Grs Method for Pareto-optimal Front Identification in Electromagnetic Synthesis
نویسندگان
چکیده
Though optimization problems in industrial electromagnetic design are often truly multiobjective, solving them by evolutionary Pareto Optimal Front approximation is often unpractical, due to the high computational cost of objective evaluations. In order to overcome this drawback, an extension of classical single-objective Generalized Response Surface (GRS) methods to Pareto-optimal front approximation is proposed. Such an extension implies essential modifications, due to the increased complexity of multiobjective optimization problems. Neural network (NN) interpolation, Pareto evolutionary search and special zooming strategies are combined in an iterative procedure, that leads to a strong reduction in true objective function calls. After a brief formal presentation of multiobjective optimization problems, and an overview of the utility of such an approach in electromagnetic design, a description of the proposed methodology is given and an electromagnetic test case is presented and solved, in order to show the validity of the strategy.
منابع مشابه
Multi-objective optimization of buckling load for a laminated composite plate by coupling genetic algorithm and FEM
In this paper, a combination method has been developed by coupling Multi-Objective Genetic Algorithms (MOGA) and Finite Element Method (FEM). This method has been applied for determination of the optimal stacking sequence of laminated composite plate against buckling. The most important parameters in optimization of a laminated composite plate such as, angle, thickness, number, and material of ...
متن کاملA Neural Network Based Generalized Response Surface Multiobjective Evolutionary Algorithm
The practical use of multiobjective optimization tools in industry is still an open issue. A strategy for reduction of objective function calls is often essential, at a fixed degree of Pareto Optimal Front (POF) approximation accuracy . To this aim an extension of single-objective NN-based GRS methods to Pareto Optimal Front (POF) approximation is proposed. Such an extension is not at all strai...
متن کاملPareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm
One of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. In this paper, the decoupled sliding mode control technique (DSMC) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. Furthermore, a new Multi-Objective Particle Swarm O...
متن کاملImprovement of Methanol Synthesis Process by using a Novel Sorption-Enhanced Fluidized-bed Reactor, Part II: Multiobjective Optimization and Decision-making Method
In the first part (Part I) of this study, a novel fluidized bed reactor was modeled mathematically for methanol synthesis in the presence of in-situ water adsorbent named Sorption Enhanced Fluidized-bed Reactor (SE-FMR) is modeled, mathematically. Here, the non-dominated sorting genetic algorithm-II (NSGA-II) is applied for multi-objective optimization of this configuration. Inlet temperature o...
متن کاملAERO-THERMODYNAMIC OPTIMIZATION OF TURBOPROP ENGINES USING MULTI-OBJECTIVE GENETIC ALGORITHMS
In this paper multi-objective genetic algorithms were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist ...
متن کامل