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.

Structural Optimization plays a critical role in improving the efficiency, cost-effectiveness, and sustainability of engineering designs. This paper presents a comparative study of single-objective and multi-objective Optimization in the structural design process. Single-objective problems focus on optimizing just one objective, such as minimizing weight or cost, while other important aspects are treated as constraints like deflections and strength requirements. Multi-objective Optimization addresses multiple conflicting objectives, such as balancing cost, with displacement treated as a secondary objective and strength requirements defined as constraints within the given limits. Both Optimization approaches are carried out using Chaos Game Optimization (CGO). While single-objective Optimization produces a definitive optimal solution that can be used directly in the final design, multi-objective Optimization results in a set of trade-off solutions (Pareto front), requiring a decision-making process based on design codes and practical criteria to select the most appropriate design. Through a real-world case study, this research will assess the performance of both Optimization strategies, providing insights into their suitability for modern structural engineering challenges.

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.