Safety as Objective in Multicriterial Structural Optimization

Physician Driven Multicriteria Optimization of Advanced Lung Cancer Patients VMAT Plans

Purpose:To present a novel multi‐criteria optimization (MCO) solution approach that generates well‐dispersed representation of the Pareto front for radiation treatment planning.Methods:Different algorithms have been proposed and implemented in commercial planning software to generate MCO plans for external‐beam radiation therapy. These algorithms consider convex optimization problems. We propose a grid‐based algorithm to generate well‐dispersed treatment plans over Pareto front. Our method is able to handle nonconvexity in the problem to deal with dose‐volume objectives/constraints, biological objectives, such as equivalent uniform dose (EUD), tumor control probability (TCP), normal tissue complication probability (NTCP), etc. In addition, our algorithm is able to provide single MCO plan when clinicians are targeting narrow bounds of objectives for patients. In this situation, usually none of the generated plans were within the bounds and a solution is difficult to identify via manual navigation. We use the subproblem formulation utilized in the grid‐based algorithm to obtain a plan within the specified bounds. The subproblem aims to generate a solution that maps into the rectangle defined by the bounds. If such a solution does not exist, it generates the solution closest to the rectangle. We tested our method with 10 locally advanced head and neck cancer cases.Results:8 objectives were used including 3 different objectives for primary target volume, high‐risk and low‐risk target volumes, and 5 objectives for each of the organs‐at‐risk (OARs) (two parotids, spinal cord, brain stem and oral cavity). Given tight bounds, uniform dose was achieved for all targets while as much as 26% improvement was achieved in OAR sparing comparing to clinical plans without MCO and previously proposed MCO method.Conclusion:Our method is able to obtain well‐dispersed treatment plans to attain better approximation for convex and nonconvex Pareto fronts. Single treatment plan can be achieved when given bounds on the objectives.

In this paper a method for including a-priori preferences of the decision makers into Multicriteria Optimization (MCO) problems is presented. A set of Pareto-optimal solutions is determined via desirability functions of the objectives which reveal experts´ preferences regarding different objective regions. By applying the method to the Binh-problem it proofs to be very effective for focusing on different parts of the Pareto front respectively the objective space of the original problem.

Formalizing Criteria of Intelligent Transportation and Logistic Systems Functioning

Genetic algorithms (GAs), which are directed stochastic hill climbing algorithms, are a commonly used optimization technique and are generally applied to single criterion optimization problems with fairly complex solution landscapes. There has been some attempts to apply GA to multicriteria optimization problems. The GA selection mechanism is typically dependent on a single-valued objective function and so no general methods to solve multicriteria optimization problems have been developed so far. In this paper, a new method of transformation of the multiple criteria problem into a single-criterion problem is presented. The problem of transformation brings about the need for the introduction of thePareto set estimation method to perform the multicriteria optimization using GAs. From a given solution set, which is the population of a certain generation of the GA, the Pareto set is found. The fitness of population members in the next GA generation is calculated by a distance metric with a reference to the Pareto set of the previous generation. As we are unable to combine the objectives in some way, we resort to this distance metric in the positive Pareto space of the previous solutions, as the fitness of the current solutions. This new GA-based multicriteria optimization method is proposed here, and it is capable of handling any generally formulated multicriteria optimization problem. The main idea of the method is described in detail in this paper along with a detailed numerical example. Preliminary computer generated results show that our approach produces better, and far more Pareto solutions, than plain stochastic optimization methods.

In the paper we present a new method for solving optimal control problems of a class of hybrid systems. We describe the new effective algorithm based on memetic algorithm (MA) for optimal control of switched systems. We concentrate on a class of problems in which a pre-specified sequence of active subsystems is known. Our aim is to find both the optimal switching instants and the optimal continuous inputs. The new approach, which we propose, decomposes the cost functional of the basic optimal control problem in Bolza form in two terms. The first term depends explicitly on a value of state variables at the final time. The second term depends on state and control trajectories. In oreder to solve those two tasks we used MA as the multi-objective optimization algorithm. In this paper we considered a fundamental bi-criteria case with two mentioned before functions: the value of state variables at the final time and the state and control trajectories. In order to find an approximation of Pareto frontier, we proposed new effective method based on genetic algorithm (GA) and local search (LS). Problems properties were taken into consideration in the design of our new approach of solving it. They were used to construct new algorithm inspired by the LS NSGA-II, which performed rather well in multi-criteria scheduling problem. Since simple genetic algorithms are efficient heuristics in searching for optimal solutions, but lack the accuracy of some more computational complex algorithms, a hybrid algorithm was constructed. It uses fast non-dominated sorting, in order to evaluate child population and allocate solutions to corresponding Pareto frontiers. I addition a local search method was used, in order to find more differentiated and better solutions. Clustering solutions from Pareto frontiers also improved diversity of solutions in child population. This approach can be used as a start point for searching for algorithm for solving optimal control problems of switched systems without pre-specified sequence of active subsystems. The performance of the algorithm is illustrated by the examples: (a) a hybrid optimal control problem with nonlinear dynamics and (b) a switched linear quadratic optimal control problem with long switching time intervals.

Already today, car manufacturers are designing E/E architectures using so-called component platforms. Such a platform comprises the superset of all components that are required to build all acquirable variants of a certain or even multiple car models. To find and optimize such component platforms, each candidate platform has to be evaluated by (a) determining a number of design objectives (monetary cost, etc.) of each car variant when derived from the candidate platform and then (b) approximating the platform's design objectives themselves, e. g., by a weighted sum that includes the expected sales of each variant. But typically, since this optimization has to take place in early design stages, important parameters like the number of expected sales numbers per car variant can only be projected and are, thus, uncertain. To investigate the susceptibility of the optimization to such uncertain parameters, this paper proposes a Monte-Carlo simulation-based method that enables to evaluate the uncertainty of a combined multi-variant objective wrt. parameter variations. By treating the minimization of uncertainty as an additional design objective, not only can the robustness of the derived component platforms be improved but also the confidence of the manufacturer. Moreover, we also propose to treat uncertainty not as a conventional design objective, but to use uncertain objectives: Here, not a single (e. g., mean) value but an interval given by observed upper and lower objective values is used. Experimental results show that the design objectives of an E/E architecture component platform are relatively robust wrt. parameter variations (here expected sales numbers of car variants). Moreover, it will be shown that the difference in expected overall costs between different non-dominated solutions is often much higher than the expected variation in cost as a result of parameter uncertainty

To evaluate dosimetric differences of IMRT lung plans generated from RayStation× Multi-Criteria Optimization (MCO) and Pinnacle Direct Machine Parameters Optimization (DMPO) Methods: Five patients previously treated with plans from Pinnacle v9.0 using DMPO algorithm were re-planned on RayStation v2.4.8.180 using MCO Pareto plans with same dose constraints, beam angles and objectives. The prescription was 60 Gy to 95% of PTV. Planning outcomes such as D95 (95% of volume receiving the prescribed dose), D2, D1 for PTV, D33 and mean dose for heart, V20 (volume receives at least 20 Gy) and mean dose for lung, max dose for cord were reported for evaluation according to our clinical protocols. The conformity of the prescription isodose volume to PTV was evaluated as conformity index (CI). Planning time was also observed. The mean volume of PTV was 356 ± 141 cm3 . The D95 of PTV with RayStation was improved and more uniform compared to plans from Pinnacle (59.4 ± 0.2 vs. 59.2 ± 0.8 Gy), respectively. D2 and D1 of PTV were lower with RayStation plans. Mean dose and V20 of total lung were lower for all RayStation plans with max dose of cord, and D33 and mean dose of heart following the same trend. CI was better with RayStation compared to Pinnacle (1.09 ± 0.06 vs. 1.16 ± 0.11). Planning time was faster with RayStation from Pinnacle (few minutes. vs. minimum of 20 minutes, approximately). MCO planning automatically generates a set of Pareto optimized solutions for given objectives to allow tradeoffs between targets and critical organs. RayStation can achieve better uniform tumor coverage with fewer hot spots while sparing more critical structures. MCO Pareto based IMRT plans is helpful in determining the best optimized dosimetry with shorter planning time.

Selection of a building direction is an important step for rapid prototyping regardless of the specific processes used to create the part. It involves the consideration of multi-factors that have influences on surface quality, build efficiency and support structure, etc. Contemporary approaches did not consider the global directional space to search for the optimal building direction. In this paper, we use a multi-sphere model for multi-criteria optimization of building direction in rapid prototyping. Each sphere represents the global directional space for one optimization criterion, and is obtained by uniformly discretizing the surface of a unit sphere. Optimization is then conducted over each discretized spherical surface for each criterion. Two objectives, theoretical volume deviation (TVD) and part height are simultaneously optimized using genetic algorithm. TVD is computed to evaluate the volumetric error along each building direction in a general way. The Pareto front is computed as well in order to study the competing effect from these two criteria. At the end of the paper, examples are presented to show the effectiveness of the method.

This paper proposes a multi-objective Green Vehicle Routing Problem (G-VRP) considering two types of vehicles likely company-owned vehicle and third-party logistics in the imprecise environment. Focusing only on one objective, especially the distance in the VRP is not always right in the sustainability point of view. Here we present a bi-objective model for the G-VRP that can address the issue of the emission of GreenHouse Gases (GHGs). We also consider the demand as a rough variable. This paper uses the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the proposed model. Finally, it uses Multicriteria Optimization and Compromise Solution (abbreviation in Serbian – VIKOR) method to determine the best alternative from the Pareto front.

Today, an important problem of the building energy performance area is carrying out multi-criteria optimizations of real building designs. To solve this problem, a new method based on a meta-model is proposed in this study. Hence, the EnergyPlus™ is used as the simulation tool for the performance simulation of the building, then a couple of the multi-criteria Modified Coot Optimization Algorithm (MCOA) dynamically combined with the artificial neural network meta-models (ANN-MM) are employed. For the sample generation applied for training and validation of ANN meta-models, an optimum way is presented by this method to minimize the whole building energy simulations needed for their training, and validate precise results of optimization. Moreover, the method is used for the thermal comfort and energy efficiency optimization of a real house to achieve the optimum balance between the heating and cooling behavior of the case building. 12 effective design variables of this case study are selected. Also, the achieved results are put in comparison with the “true” Pareto front found through an optimization method based on simulation performed for more validation. It is assumed that 1280 points are adequate in this case study to obtain precise results on the Pareto set. Thus, 75% of the required simulations’ number based on physics has been saved by this size of sample considering the 5120 applied in the method based on simulation. Consequently, the optimum Pareto set of a real multi-criteria building efficiency optimization problem is achieved by the proposed method and accurate results are achieved.

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Back et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint 27

Eurocode conforming design of BRBF – Part I: Proposal for codification

To evaluate the intraocular pressure (IOP)-lowering effect and safety of up to 2 bimatoprost implant administrations versus selective laser trabeculoplasty (SLT). Phase 3 (Stage 2), randomized, 24-month, multicenter, patient- and efficacy evaluator-masked, paired-eye clinical trial (NCT02507687). Patients (n = 183) with open-angle glaucoma or ocular hypertension inadequately managed with topical IOP-lowering medication for reasons other than efficacy. Patients received a single 360° SLT procedure in 1 eye and 10-µg bimatoprost implant administration in the contralateral eye. Initially, implant-treated eyes received a second implant at week 16 if safety criteria were met. After a protocol amendment, implant-treated eyes were retreated with flexible scheduling if IOP was >17 mm Hg and safety criteria were met. The primary efficacy variable was IOP change from baseline, with primary timepoints at weeks 4, 12, and 24. Safety measures included treatment-emergent adverse events (TEAEs) and ocular safety measures. Mean (±SE) baseline IOP (mm Hg) was 25.2 ± 0.22 and 25.1 ± 0.22 in implant- and SLT-treated eyes, respectively. Least-squares mean (±SE) IOP reduction from baseline (mm Hg) for eyes treated with up to 2 implants versus SLT was 6.8 ± 0.28 versus 6.2 ± 0.28 at week 4, 6.9 ± 0.30 versus 6.4 ± 0.30 at week 12, and 6.9 ± 0.27 versus 6.5 ± 0.28 at week 24. The probability of not having required nonstudy (rescue) IOP-lowering treatment at days 360 and 720, respectively, was 67.5% and 50.2% for implant-treated eyes versus 68.7% and 60.6% for SLT-treated eyes. The most common ocular TEAE in both implant- and SLT-treated eyes was increased IOP attributed to wearing off of efficacy. Mean (±SE) percentage change in corneal endothelial cell density from baseline at month 24 was -6.2 ± 1.13% in implant-treated eyes (-7.9 ± 2.04% with fixed readministration; -5.2 ± 1.35% with flexible readministration) versus -3.1 ± 0.43% in SLT-treated eyes. The bimatoprost implant demonstrated statistical and clinical noninferiority to SLT in IOP reduction from baseline at weeks 4, 12, and 24. In subgroup analysis, patients with flexible implant readministration met the same criteria. Both the implant and SLT demonstrated sustained (2-year) IOP lowering in many eyes. A flexible administration schedule improved the safety profile of the implant over the fixed administration schedule.

A basic idea common to all standard design procedures is, as previously explained, the preparation of reusable, prefabricated and verified circuit components [3.1]. The design expenditure for the layout is saved. And the design engineer can work at a higher abstraction level. Transistors and the technological limitations — described in terms of process technology related design rules — no longer form the design basis; rather it is logic elements such as gates, flipflops and memories. These elements are called cells. That is why standard-design procedures are also called cell-oriented design procedures.