Multiobjective Design Optimization of Reentrant Auxetic Model Using Lichtenberg Algorithm Based on Metamodel

The optimization of five different responses of an auxetic model was considered: mass; critical buckling load under compression effort; natural frequency; Poisson’s ratio; and failure load. The Response Surface Methodology was applied, and a new meta-heuristic of optimization called the Multi-Objective Lichtenberg Algorithm was used to find the optimized configuration of the model. It was possible to increase the failure load by 26,75% in compression performance optimization. Furthermore, in the optimization of modal performance, it was possible to increase the natural frequency by 37.43%. Finally, all 5 responses analyzed simultaneously were optimized. In this case, it was possible to increase the critical buckling load by 42.55%, the failure load by 28.70% and reduce the mass and Poisson’s ratio by 15.97% and 11%, respectively. This paper shows something unprecedented in the literature to date when evaluating in a multi-objective optimization problem, the compression and modal performance of an auxetic reentrant model.

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

Multiobjective design of sustainable public transportation systems

The design of the bus network is a complex problem in modern cities, since different conflicting objectives have to be considered, from both the perspective of bus companies and the citizens. This article presents a multiobjective model for designing a sustainable public transportation network that simultaneously optimizes the covered travel demands by passengers, the total travel time, and the generated pollution. The proposed model is solved using exact weighted sum and a heuristic procedure based on the standard shortest path problem. Preliminary tests were performed in small real-world instances of Montevideo, Uruguay. Experiments allowed obtaining a set of compromising solutions that in turn allow exploring different trade-off among the optimization criteria. The proposed heuristic was competitive, being able to find a good compromising solution in short computing times.

Multicriteria optimal selection of a hydraulic cylinder for drive mechanisms

The paper reports the optimization synthesis of a hydraulically actuated drive mechanism. A mathematical model of the mechanism using vector closure equations is developed. Based on the functional purpose of the mechanism, a set of geometric and force/moment requirements are defined which must be met by a proper selection of a standardized hydraulic cylinder and its points of attachment. A multiobjective design optimization task is defined with three objective functions whose minimum is searched - the mass of the hydraulic cylinder, the squared total deviation of the developed by the hydraulic cylinder moments from the predefined values of the external moments and the force in the hydraulic cylinder. The defined multiobjective optimization task is considered as a mixed variable nonlinear constrained optimization problem containing 5 continuous and 2 discrete variables and the multistage Monte Carlo method is used for its solution. Using different weighting schemes several Pareto-optimal compromise solutions are obtained.

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and...