Selecting a genuine objective function in the fuel management optimization (FMO) of newly developed reactors is fundamentally important. The FMO problem becomes harder when a Multi-objective fitness (cost) function (MOCF) is in use. Usually, when undertaking a MOCF fuel management optimization problem, it is transformed into the summation of objective functions, which are related to weighting factors. Different parameters can be chosen as the main fitness function in an optimization problem. In the case of a nuclear reactor, the cycle length, the Multiplication factor and power peaking factor are the most significant. The value of the weighting factors and/or the method with which the cost function has been formulated may affect the final result of optimization. In this paper, the effect of the selection of the cost function has been analyzed in order to reach an optimum in core fuel management of a typical pressurized water reactor, PWR. It is understood from the results that finding a loading pattern that results in a better power peaking factor (lower PPF) is stricter than that of a longer cycle length. Indeed, the obtained loading pattern strongly depends on the selected fitness function. Finally, the flattening function is proposed instead of minimizing the PPF to attain better loading patterns.

Multi-hole orifices have better performance than single-hole orifices. In this paper, Multi-objective optimization of Multi-hole orifices is performed using a Fluid-Solid Interaction (FSI) analysis and Multi-objective genetic algorithm (NSGA II). In all numerical analysis, the governing equations of the solid and the governing equations of the fluid are carried out for orifice and fluid around orifice respectively. All calculations are made for a 16-hole orifice with circular holes. The design variable in the optimization process is the distance between the holes of the orifice and thus the amount of shrinkage or expansion of the orifice geometry. The objective functions are the pressure drop created on the sides of the orifice, the deformation and tension created in the orifice structure, which should be maximized, minimized and minimized respectively. In the results section, the Pareto front are presented which represent useful information for designing the Multi-hole orifices geometry, and five orifices are also introduced as final design options that have better performance. The results of the sensitivity analysis of the various parameters are also presented and discussed in detail in the Multi-hole orifices.

This study aims to provide an e cient Multi-objective Multidisciplinary Robust Design optimization (MOMRDO) framework. To this end, Bi-Level Integrated System Synthesis (BLISS) framework was implemented as a fast Multi-disciplinary Design optimization (MDO) framework. Progressive Latin Hypercube Sampling (PLHS) was developed as a Design Of Experiment (DOE) for the Uncertainty Analysis (UA). This systematic approach leads to a fast, adaptive, and e cient Robust Design optimization (RDO) framework of complex systems. The accuracy and performance of the proposed algorithm have been evaluated through various tests. Finally, the RDO of a hydrazine monopropellant thruster was selected as a case study. The results showed that this method is a fast and e ective method for the Multi-objective optimization design of complex systems, and it can also be used in other engineering applications.

Here we discuss the problem of distribution of Real time processes on Multiprocessor with on-time maximum job accomplished. Scientists have been searching for producing optimized scheduling. This is an example of NP problems. This is not practical to approach this kind of problems with heuristic approach thus we must use meta-heuristic algorithms. These algorithms present many sets of answers in order to make options for scheduler, to choose the best process assignment to processor. Two examples are Branch and Bound, and Task Graph Algorithms. By studying the ant colony, Genetics and PSO Algorithms, we will design and consider several methods for our purpose and use them to produce Job assignment Scheduler, on processors. Each of these algorithms will provide us with a specific designing method and help us to make a scheduler engine of real time processes assignment on processors. We will compare each program to the first heuristic one, to assess the manufactured programs. In comparisons which are based on lost processes, Colony approach has 11.94 %, PSO approach 11.19 %, and Genetic approach has 7.52 % less process lost in compare to heuristic approach. It worth mention that 20 files each of which containing 50 Real time process have been used in these experiments.

This paper introduces a novel Multi-objective evolutionary algorithm based on cat swarm optimization algorithm (EMCSO) and its application to solve a Multi-objective knapsack problem. The Multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses the nondominated sorting method to find the solutions as close as to POF and crowding distance technique to obtain a uniform distribution among the non-dominated solutions. Also, the algorithm is allowed to keep the elites of population in reproduction process and use an opposition based learning method for population initialization to enhance the convergence speed. The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based on performance measures of generational distance (GD), inverted GD, spread, and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve Multi-objective knapsack problem.