A Novel Approach to Configuration Redesign: Using Multiobjective Monotonicity Analysis to Alter the Pareto-set

Configuration (or topology or embodiment) design remains a ubiquitous challenge in product design optimization and in design automation, meaning configuration design is largely driven by experience in industrial practice. In this article, we introduce a novel configuration redesign process founded on the interaction of the designer with results from rigorous multiobjective monotonicity analysis. Guided by Pareto-set dependencies, the designer seeks to reduce trade-offs among objectives or improve optimality overall, deriving redesigns that eliminate dependencies or relax active constraints. The method is demonstrated on an ingestible medical device for oral drug delivery, currently in early concept development.

A Fuzzy Version of the TOPSIS Method in Multi-Criteria Decision-Making Tasks

Choice and decision making are an integral part of the purposeful activities of people in all areas of public and private life. Tasks of multi-criteria decision making are characterised by the fact that alternative decisions are evaluated by a set of criteria and the concept of a decision and its outcome coincide. The defining concept in such problems is the concept of a set of Pareto optimal decisions (Pareto set). This set forms alternative decisions that are not comparable in terms of the set of evaluation criteria. The choice of the optimal decision in the Pareto set can be performed only on the basis of the subjective preferences of the decision maker. In recent decades, extensions of traditional methods of multi-criteria decision making to a fuzzy environment have been proposed. One of the well-known approaches to multi-criteria decision making is the TOPSIS method. In the paper, a fuzzy version of this method is considered in situations where the values of evaluation criteria are set in the form of fuzzy numbers.

LOGICAL ANALYSIS OF DATA WITH PARETO OPTIMAL PATTERNS SEARCH

Предлагается способ генерации логических закономерностей через аппроксимацию множества Парето эвристическим алгоритмом NSGA-II. Указанный метод применяется для решения задачи медицинской диагностики. The paper focuses on logical patterns generation as Pareto set approximation using heuristic algorithm NSGA-II. This method is used to solve the problem of medical diagnostics.

Multiobjective Monotonicity Analysis: Pareto Set Dependency and Tradeoffs Causality in Configuration Design

Multiobjective design optimization studies typically derive Pareto sets or use a scalar substitute function to capture design trade-offs, leaving it up to the designer's intuition to use this information for design refinements and decision making. Understanding the causality of trade-offs more deeply, beyond simple post-optimality parametric studies, would be particularly valuable in configuration design problems to guide configuration redesign. This paper presents the method of Multiobjective Monotonicity Analysis to identify root causes for the existence of trade-offs and the particular shape of Pareto sets. This analysis process involves reducing optimization models through constraint activity identification to a point where dependencies specific to the Pareto set and the constraints that cause them are revealed. The insights gained can then be used to target configuration design changes. We demonstrate the proposed approach in the preliminary design of a medical device for oral drug delivery

Formation of the Optimal Load of High School Students Using a Genetic Algorithm and a Neural Network

The problem of the effectiveness of teaching can be successfully solved only if the high quality of lessons is supported by well-organized homework of students. The question of homework occupies one of the main places in educational activities since this question is directly related to the health of the child. A competent approach to minimizing the time for completing homework, taking into account the maximum efficiency obtained from the learning process, can preserve the health of students to some extent. The article describes a method for obtaining the most comfortable results of the process of completing homework, which are a Pareto set. This method is implemented using a genetic algorithm and queuing theory, and the selection of homework is carried out on the basis of intellectual analysis of the text of tasks and is a scale of a certain range. The proposed algorithm successfully obtains the solutions of the Pareto set and minimizes the efforts of school students while achieving the maximum efficiency of the educational process to preserve their health. Compared with other known algorithms, the results obtained show that the proposed algorithm demonstrates fairly accurate optimization characteristics presented in the form of a Pareto set. Furthermore, combining a genetic algorithm, queuing theory apparatus, and a neural network makes it possible to model the studied subject area more accurately.

Design and Shape Optimization of a Biodegradable Polymeric Stent for Curved Arteries Using FEM

Stent treatment has revealed safe and efficient outcomes for straight arteries, while it is still challenging for curved coronary arteries. On the one hand, a stent should be flexible enough to take the artery’s curvature with the least stress to the artery wall. On the other hand, it has to be strong enough to prevent any artery diameter reduction after the implant. In this work, the genetic algorithm multi-objective optimization method is exploited to provide a Pareto set and to design a curvature stent. The design has been performed based on the appropriate flexibility and radial strength design, depending on the characteristics of a particular case study. In the optimization procedure, flexibility and radial strength have been evaluated based on ASTM standard mechanical tests. These tests have been parametrically simulated using the finite element method. The strut curvature is formed by the spline curvature, whose middle point coordinates are two of the optimization variables. The other optimization variable is the thickness of the stent. Based on the Pareto set achieved from the optimization, five different stent designs have been proposed. In these designs, the middle part of the stent is stiffer (in the plaque aggregated) and benefits more radial strength rather than flexibility. At the stent’s extremes, where more deformation takes place, flexibility is weighted more than radial strength. These five design sets differ in their objective weight ratios. At the end of this research, their implementation in a curved vessel is simulated in ABAQUS/CAE, and von Mises stress distribution, maximum von Mises stress, and stent recoil after imposing the stent have been analyzed. The obtained Pareto front can also be a useful guide for physicians to design and manufacture customized stents for each patient.

The Influence of Diesel–Ethanol Fuel Blends on Performance Parameters and Exhaust Emissions: Experimental Investigation and Multi-Objective Optimization of a Diesel Engine

Compression combustion engines are a source of air pollutants such as HC and Co, but are still widely used throughout the world. The use of renewable fuels such as ethanol, which is a low-carbon fuel, can reduce the emission of these harmful gases from the engine. A fundamental analysis is proposed in this research to experimentally examine the emission characteristics of diesel–ethanol fuel blends. Furthermore, a multi-objective genetic algorithm (e-MOGA) was developed based on the experimental data obtained to fine the most effective or Pareto set of engine emission and performance optimization solutions. So, the optimization problem had two inputs and seven objectives. For this purpose, input variables for the search space were S (rpm) varied in the range of (1600–2000) and E (%) varied in the range of (0–12). These design variables were chosen to be varied in a prespecified range with a lower and upper band as same as experimental conditions. A diesel engine using (DE2, DE4, DE6, DE8, DE10, and DE12) diesel–ethanol fuel blends, at the various speed of 1600 to 2000 rpm, was utilized for the experiment. The findings showed that the use of diesel–ethanol fuel blends decreased the concentration of CO and HC emissions by 3.2–30.6% and 7.01–16.25%, respectively, due to the high oxygen content of ethanol. As opposed to CO and HC emissions, the NOx concentration showed an increase of 7.5–19.6%. This increase was attributed to the high combustion quality in the combustion chamber, which resulted in a higher combustion chamber temperature. The optimization results confirmed that the shape of the Pareto front obtained from multi-objective ϵ-Pareto optimization could be convex, concave, or a combination of both. A new parameter was introduced as emission index or EI for selection of the best solution among the Pareto set of solutions. This parameter had a minimum value of 4.61. The variables levels for this optimum solution were as follows: engine speed = 1977 rpm, ethanol blend ratio = 10%, CO = 0.27%, CO2 = 6.81%, HC = 3 ppm, NOx = 1573 ppm, SFC = 239 g/kW·h, P = 56 kW, and T = 269.9 N·m. The EI index had a maximum value of 8.26. Conclusively, we can say that the optimization algorithm was successful in minimizing emission index for all ethanol blend ratios, especially at higher engine speeds.