many studies are performed by researchers about shell and tube heat exchanger but the multi-objective big bang-big crunch algorithm (mobba) technique has never been used in such studies. this paper presents application of thermal-economic multi-objective optimization of shell and tube heat exchanger using mobba. for optimal design of a shell and tube heat exchanger, it was first thermally model...

By replacing the selection component, a well researched evolutionary algorithm for scalar optimization problems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators ...

In this study a multi-objective optimization model is developed for water sensor network design in water distribution systems. In this model the three criteria used for evaluating the performance of the water sensor placement designed are directly used as the objectives of the optimization problem. These include minimizing the expected water volume contaminated, minimizing the expected time of ...

The purpose of this paper is to examine the multidisciplinary design optimization (MDO) of a reentry vehicle. In this paper, optimization of a RV based on, minimization of heat flux integral and minimization of axial force coefficient integral and maximization of static margin integral along reentry trajectory is carried out. The classic optimization methods are not applicable here due to the c...

The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator...

Feature selection for transient classification is the problem of choosing among several monitored parameters (i.e., the features) those to be used for efficiently recognizing the developing transient patterns. It is a critical issue for the application of “on condition” diagnostic techniques in complex systems, like the nuclear power plants, where hundreds of parameters are measured. Indeed, ir...

In this paper, an improved multi-objective differential evolution algorithm(IDEA) is proposed for multi-objective optimization problems. In IDEA, the select operator combines the advantages of DE with the mechanisms of Pareto-based ranking and distance density, besides, a randomly migration strategy is proposed. IDEA is implemented on four classical multi-objective problems, the simulation resu...

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill...

There are currently three different game strategies to solve multi-criteria design problems: 1) Cooperative Games (Pareto Front), 2) Competitive Games (Nash Game) and 3) Hierarchical Games (Stackelberg Game). Nowadays EAs and Pareto front concept are more and more in solving practical design problems in industry, but this process is time consuming. Here, deterministic optimization method and Pa...