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.

In order to improve the performance of single-stage turbines, blade profiling optimization was conducted for the guide vane and rotor under design condition. Support vector regression (SVR) and non-dominated sorting genetic algorithm-II (NSGA-II) were used to execute the optimization, with the objective of maximizing the efficiency and total pressure ratio of single-stage turbines. The gas turbine chosen for the initial study was the KJ66, which is one of the most robust and primitive small gas turbine designs available. The influence mechanism of the stator and rotor profiling on flow field and performance was discussed. The results revealed that compared with the prototype, the adiabatic efficiency increased by 5. 95% and the total pressure ratio increased by 0. 9%. Furthermore, the matching of flows between the stator blade and rotor blade obviously improved. The optimized guide vane suppressed the flow separation by increasing the leading edge and improving the distribution of the inlet angle of attack. The load distribution of rotors with a 50% spanwise position changed from the original "C" loaded to post-loaded. The leading load obviously decreased, and the angle of attack was smaller than that of the prototype, which effectively weakened the flow separation at the leading edge of the rotor. Compared with the original rotor, the higher lean angle and pressure ratio of the turbine stage also improved. However, the leakage loss near the shroud of the rotor increased, which led to decreased efficiency.

Application of either protein or carbohydrate-based products as fat replacers in low fat ice-creams can improve the properties of these products. However, the type and level of fat and fat replacer utilized are affected by such different parameters as functional ones, namely: viscosity and overrun, hardness and melting rate, nutritional properties (calories) as well as the price of the final product. Throughout the present study, single and multi-objective optimization method as based on the genetic algorithms (GAs) was applied to select the suitable fat-free as well as low-fat ice-cream formulations. The data related to single-objective optimization of selected parameters revealed that the ice creams containing 3.5% Simplesse plus 1.72% fat, and 2.95% Maltodextrin plus 1.87% fat have ended up with the most desirable functional objectives. The application of multi-objective optimization led to a range of solutions of different fat and fat replacer contents out of which the producers can adopt the most suitable choice depending on the needs.

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.

In this paper, two recent heuristic optimization algorithms are presented to optimally manage the operation of the microgrid(MG) with installed renewable energy sources (RESs); krill herd (KH) optimization and ant lion optimizer (ALO)algorithms. The first algorithm is used for solving single-objective function represents either total operation cost or totalpollutant emission injected from the installed generating units while ALO is applied to solve the multi-objective function ofboth total operating cost and emission. The problem is formulated as nonlinear constrained objective function with equalityand inequality constraints. In this work; the devices installed in MGs are photovoltaic panel (PV), wind turbine (WT), microturbine(MT), fuel cell (FC), battery and grid. Two scenarios are studied; the first one is optimizing MG with installing allRESs within specified limits in addition to grid, while the second scenario is operating both PV and WT at their rated powers. The obtained results are compared with different reported algorithms like genetic algorithm (GA), Fuzzy self-adaptivePSO (FSAPSO) and others programmed like particle swarm optimization (PSO), grey-wolf optimizer (GWO) and whaleoptimization algorithm (WOA). For first scenario; the proposed KH gives the best optimal cost of 105. 94 € ct while the bestemission is 420. 57 kg, the best optimal cost and emission of 592. 86 € ct 339. 71 kg are obtained via KH in the second scenario.