Multi-criteria optimization (MCO) function has been available on commercial radiotherapy (RT) treatment planning systems to improve plan quality; however, no study has compared Eclipse and RayStation MCO functions for prostate RT planning. The purpose of this study was to compare prostate RT MCO plan qualities in terms of discrepancies between Pareto optimal and final deliverable plans, and dosimetric impact of final deliverable plans. In total, 25 computed tomography datasets of prostate cancer patients were used for Eclipse (version 16.1) and RayStation (version 12A) MCO-based plannings with doses received by 98% of planning target volume having 76 Gy prescription (PTV76D98%) and 50% of rectum (rectum D50%) selected as trade-off criteria. Pareto optimal and final deliverable plan discrepancies were determined based on PTV76D98% and rectum D50% percentage differences. Their final deliverable plans were compared in terms of doses received by PTV76 and other structures including rectum, and PTV76 homogeneity index (HI) and conformity index (CI), using a t-test. Both systems showed discrepancies between Pareto optimal and final deliverable plans (Eclipse: -0.89% (PTV76D98%) and -2.49% (Rectum D50%); RayStation: 3.56% (PTV76D98%) and -1.96% (Rectum D50%)). Statistically significantly different average values of PTV76D98%,HI and CI, and mean dose received by rectum (Eclipse: 76.07 Gy, 0.06, 1.05 and 39.36 Gy; RayStation: 70.43 Gy, 0.11, 0.87 and 51.65 Gy) are noted, respectively (p < 0.001). Eclipse MCO-based prostate RT plan quality appears better than that of RayStation.

Agricultural production consumes the majority of global freshwater resources. The worsening water scarcity has imposed significant stress on agricultural production when regions seek food self-sufficiency. To seek optimal allocation of spatial agricultural water and land resources in each water function zone of the objective region, a multi-objective optimization model was developed to tackle the trade-offs between the water-saving objective and the economic benefit objective considering virtual water trade (VWT). The cultivated area of each crop in each water function zone was taken into account as the decision variable, while a set of strong constraints were used to restrict land resources and water availability. Then, a decomposition-simplex method aggregation algorithm (DSMA) was proposed to solve this nonlinear, bounding-constrained, and multi-objective optimization model. Based on the quantitative analysis of the spatial blue and green virtual water in each agricultural product, the proposed methodology was applied to a real-world, provincial-scale region in China (i.e., Jiangsu Province). The optimized results provided 18 Pareto solutions to reallocate the land resources in the 21 IV-level water function zones of Jiangsu Province, considering four major rainy-season crops and two dry-season crops. Compared to the actual scenario, the superior scheme increased by 7.95% (5.6 × 109 RMB) for economic trade and decreased by 1.77% (2.0 × 109 m3) for agricultural water consumption. It was mainly because the potential of spatial blue and green virtual water in Jiangsu was fully exploited by improving spatial land resource allocation. The food security of Jiangsu could be guaranteed by achieving self-sufficiency in the superior scheme, and the total VWT in the optimal scheme was 2.2 times more than the actual scenario. The results provided a systematic decision-support methodology from the perspective of spatial virtual water coordination, yet, the methodology is widely applicable.

Combined cooling, heating and power (CCHP) is one of methods for enhancing the efficiency of the energy conversion systems. In this study a CCHP system consisting of a gas turbin (GT) as the topping cycle, and an organic Rankine cycle (ORC) associated with double-effect absorbtion chiller (DEACH) is decisioned as the bottoming cycle to recover the waste heat from GT exhaust gas. The considered CCHP system is investigated to maintain electricity, heating and cooling demand of a town. A parametric study is investigated and the effect decision variables on the performance indicators including exergy efficiency, total cost rate (TCR), cooling capacity, and ORC power generation is examined. Decision variables of the ORC system consist of HRVG pressure, and condenser pressure and the DEACH including evaporator pressure, condseser pressure, concentration of the concentrated solution, concentration of the weak solution, and solution mass flow rate. Finally a multi-objective optimization performed using Genetic Algorithm (GA) and the optimal design point is selected. It is observed at the optimum point the exergy efficiency, TCR, and sustainability index are 17.56%, 74.49 $/h, and 1.21, respectively.

The use of renewable fuels leads to reduction in the use of fossil fuels and environmental pollutants. In this study, the design and analysis of a CCPP based on the use of syngas produced from biomass is discussed. The studied system includes a gasifier system to produce syngas, an external combustion gas turbine and a steam cycle to recover waste heat from combustion gases. Design variables include syngas temperature, syngas moisture content, CPR, TIT, HRSG operating pressure, and PPTD. The effect of design variables on performance components such as power generation, exergy efficiency and total cost rate of the system is investigated. Also, through multi-objective optimization, the optimal design of the system is done. Finally, it is observed that at the final decisioned optimal point, the produced power is 13.4 MW, the exergy efficiency is 17.2%, and the TCR is 118.8 $/h.

With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application.

Generating feasible solution and selecting valuable solution are the most important issues when dealing with complicated multi-objective problems. Focusing on these issues, the mechanism of multi-objective problem is analyzed by evolutionary history and environmental information. Hierarchical decision based on rank fitness of distance correlation is proposed to guide the evolutionary operator. Heuristic learning by dynamic evolutionary is introduced to deal with static optimization problem. History information acquired from solution landscape is used to achieve a comprehensive search on feasible region. Based on these improvement, multi-objective evolutionary algorithm based on hierarchical decision, heuristic learning and historical environment (MOEA3H) is proposed. The proposed algorithm performs best on 10 and 14 of 19 test problems on IGD and Hvpervolume, respectively.

In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The GSDP method is compared with the NSGA-II method using multi-objective problems in the DTLZ series, and the results show the GSDP method saved computational cost by more than 85% compared to NSGA-II method. A diversity comparison indicator (DCI) is used to evaluate approximate Pareto fronts. The comparison results show the diversity performance of GSDP is better than that of NSGA-II in most cases. We demonstrate the proposed GSDP method using a practical multi-objective design example of EM-based UWB antenna for IoT applications.