Truss topology optimization using an improved species-conserving genetic algorithm

YesThe aim of this article is to apply and improve the species-conserving genetic algorithm (SCGA) to search multiple solutions of truss topology optimization problems in a single run. A species is defined as a group of individuals with similar characteristics and is dominated by its species seed. The solutions of an optimization problem will be selected from the found species. To improve the accuracy of solutions, a species mutation technique is introduced to improve the fitness of the found species seeds and the combination of a neighbour mutation and a uniform mutation is applied to balance exploitation and exploration. A real vector is used to represent the corresponding cross-sectional areas and a member is thought to be existent if its area is bigger than a critical area. A finite element analysis model was developed to deal with more practical considerations in modelling, such as the existence of members, kinematic stability analysis, and computation of stresses and displacements. Cross-sectional areas and node connections are decision variables and optimized simultaneously to minimize the total weight of trusses. Numerical results demonstrate that some truss topology optimization examples have many global and local solutions, different topologies can be found using the proposed algorithm on a single run and some trusses have smaller weights than the solutions in the literature

Application of reduced-set pareto-lipschitzian optimization to truss optimization

In this paper, a recently proposed global Lipschitz optimization algorithm Pareto-Lipschitzian Optimization with Reduced-set (PLOR) is further developed, investigated and applied to truss optimization problems. Partition patterns of the PLOR algorithm are similar to those of DIviding RECTangles (DIRECT), which was widely applied to different real-life problems. However here a set of all Lipschitz constants is reduced to just two: the maximal and the minimal ones. In such a way the PLOR approach is independent of any user-defined parameters and balances equally local and global search during the optimization process. An expanded list of other well-known DIRECT-type algorithms is used in investigation and experimental comparison using the standard test problems and truss optimization problems. The experimental investigation shows that the PLOR algorithm gives very competitive results to other DIRECT-type algorithms using standard test problems and performs pretty well on real truss optimization problems

Structural optimization using evolutionary multimodal and bilevel optimization techniques

This research aims to investigate the multimodal properties of structural optimization using techniques from the field of evolutionary computation, specifically niching and bilevel techniques. Truss design is a well-known structural optimization problem which has important practical applications in many fields. Truss design problems are typically multimodal by nature, meaning that it offers multiple equally good design solutions with respect to the topology and/or sizes of the members, but they are evaluated to have similar or equally good objective function values. From a practical standpoint, it is desirable to find as many alternative designs as possible, rather than finding a single design, as often practiced. Niching is an intuitive way of finding multiple optimal solutions in a single optimization run. Literature shows that existing niching methods are largely designed for handling continuous optimization problems. There does not exist a well-studied niching method for constrained discrete optimization problems like truss design problems. In addition, there are no well-defined multimodal discrete benchmark problems that can be used to evaluate the reliability and robustness of such a niching method. This thesis fills the identified research gaps by means of five major contributions. In the first contribution, we design a test suite for producing a diverse set of challenging multimodal discrete benchmark problems, which can be used for evaluating the discrete niching methods. In the second contribution, we develop a binary speciation-based PSO (B-SPSO) niching method using the concept of speciation in nature along with the binary PSO (BPSO). The results show that the proposed multimodal discrete benchmark problems are useful for the evaluation of the discrete niching methods like B-SPSO. In light of this study, a time-varying transfer function based binary PSO (TVT-BPSO) is developed for the B-SPSO which is the third contribution of this thesis. We propose this TVT-BPSO for maintaining a better balance between exploration/exploitation during the search process of the BPSO. The results show that the TVT-BPSO outperforms the state-of-the-art discrete optimization methods on the large-scale 0-1 knapsack problems. The fourth contribution is to consider and formulate the truss design problem as a bilevel optimization problem. With this new formulation, truss topology can be optimized in the upper level, at the same time the size of that truss topology can be optimized in the lower level. The proposed bilevel formulation is a precursor to the development of a bilevel niching method (Bi-NM) which constitutes the fifth contribution of this thesis. The proposed Bi-NM method performs niching at the upper level and a local search at the lower level to further refine the solutions. Extensive empirical studies are carried out to examine the accuracy, robustness, and efficiency of the proposed bilevel niching method in finding multiple topologies and their size solutions. Our results confirm that the proposed bilevel niching method is superior in all these three aspects over the state-of-the-art methods on several low to high-dimensional truss design problems

Seeking multiple solutions:an updated survey on niching methods and their applications

Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving

Light-Weight Structural Optimization Through Biomimicry, Machine Learning, and Inverse Design

In load-bearing lightweight architectures, cellular materials were frequently utilized. While octahedron, tetrahedron, and octet truss lattice truss were built for lightweight architectures with stretching and flexural dominance, it can be believed that new cells could easily be designed that might perform much better than the present ones in terms of mechanical and architectural characteristics. Machine learning-based structure scouting and design improvisation for better mechanical performance is a growing field of study. Additionally, biomimicry—the science of imitating nature’s elements—offers people a wealth of resources from which to draw motivation as they work to create a better quality of life. Here, utilizing machine learning approaches, novel lattice truss unit cellular architectures with enhanced architectural characteristics were designed. An inverse design methodology employing generative adversarial networks is suggested to investigate and improvise the lightweight lattice truss unit cellular architectures. The proposed framework was utilized to identify various lattice truss unit cellular architectures with load carrying capacities 40–120% greater than those of octet unit cells. A further 130–160% raise in buckling load bearing capacity was made possible by substituting porous biomimicry columns for the solid trusses in the light-weight lattice truss unit cellular architectures. This dissertation\u27s main goal is to investigate various improvisation strategies for creating lightweight architectures, particularly when using data analysis and machine learning methods. Lightweight cellular architectures with thin-walls and lattice truss unit cellular architectures with improved shape memory capabilities were created using the knowledge gleaned from numerous of the research projects mentioned in the preceding paragraphs load-bearing architectures and devices, lightweight architecture with shape memory and with better strength, better stretchability, and better elastic stress recovery are widely desired. As compared to the bulk shape memory polymeric cylinders, the cellular architectures with thin walls show 200% betterer elastic stress recovery that is normalized with respect to base designs. The architectural improvisation of many other additional designs and practical implementation can be accomplished using the inverse design framework

Modelling and optimisation of a multistage Reverse Osmosis processes with permeate reprocessing and recycling for the removal of N-nitrosodimethylamine from wastewater using Species Conserving Genetic Algorithms

YesThe need for desalinated seawater and reclaimed wastewater is increasing rapidly with the rising demands for drinkable water required for the world with continuously growing population. Reverse Osmosis (RO) processes are now among the most promising technologies used to remove chemicals from industrial effluents. N-nitrosamine compounds and especially N-nitrosodimethylamine (NDMA) are human carcinogens and can be found in industrial effluents of many industries. Particularly, NDMA is one of the by-products of disinfection process of secondary-treated wastewater effluent with chloramines, chlorines, and ozone (inhibitors). However, multi-stage RO processes with permeate reprocessing and recycling has not yet been considered for the removal of N-nitrosodimethylamine from wastewater. This research therefore, begins by investigating a number of multi-stage RO processes with permeate-reprocessing to remove N-nitrosodimethylamine (NDMA) from wastewater and finds the best configuration in terms of rejection, recovery and energy consumption via optimisation. For the first time we have applied Species Conserving Genetic Algorithm (SCGA) in optimising RO process conditions for wastewater treatment. Finally, permeate recycling is added to the best configuration and its performance is evaluated as a function of the amount of permeate being recycled via simulation. For this purpose, a mathematical model is developed based on the solution diffusion model, which is used for both optimisation and simulation. A number of model parameters have been estimated using experimental data of Fujioka et al. (Journal of Membrane Science 454 (2014) 212–219), so that the model can be used for simulation and optimisation with high accuracy and confidence

Hybrid Modelling and Optimisation of Oil Well Drillstrings

The failure of oil well drillstrings due to torsional and longitudinal stresses caused by stick-slip phenomena during the drilling operation causes great expense to industry. Due to the complicated and harsh drilling environment, modelling of the drillstring becomes an essential requirement in studies. Currently, this is achieved by modelling the drillstring as a torsional lumped model (which ignores the length of the drillstring) for real-time measurement and control. In this thesis, a distributed-lumped model including the effects of drillstring length was developed to represent the drillstring, and was used to simulate stick-slip vibration. The model was developed with increasing levels of detail and the resultant models were validated against typical measured signals from the published literature. The stick-slip model describes the friction model that exists between the cutting tool and the rock. Based on theoretical analysis and mathematical formulation an efficient and adaptable model was created which was then used in the application of a method of species conserving genetic algorithm (SCGA) to optimise the drilling parameters. In conclusion, it was shown that the distributed-lumped model showed improved detail in predicting the transient response and demonstrated the importance of including the drillstring length. Predicting the response of different parameters along the drillstring is now possible and this showed the significant effect of modelling the drillcollar. The model was shown to better represent real system and was therefore far more suited to use with real time measurements.Iraqi Government, Ministry of Higher Education and Scientific Research

An algorithmic approach to system architecting using shape grammar-cellular automata

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (p. 404-417).This thesis expands upon the understanding of the fundamentals of system architecting in order to more effectively apply this process to engineering systems. The universal concern about the system architecting process is that the needs and wants of the stakeholders are not being fully satisfied, primarily because too few design alternatives are created and ambiguity exists in the information required. At the same time, it is noted that nature offers a superb example of system architecting and therefore should be considered as a guide for the engineering of systems. Key features of nature's architecting processes include self-generation, diversity, emergence, least action (balance of kinetic and potential energy), system-of-systems organization, and selection for stability. Currently, no human-friendly method appears to exist that addresses the problems in the field of system architecture while at the same time emulating nature's processes. By adapting nature's self-generative approach, a systematic means is offered to more rigorously conduct system architecting and better satisfy stakeholders. After reviewing generative design methods, an algorithmic methodology is developed to generate a space of architectural solutions satisfying a given specification, local constraints, and physical laws. This approach combines a visually oriented human design interface (shape grammar) that provides an intuitive design language with a machine (cellular automata) to execute the system architecture's production set (algorithm). The manual output of the flexible shape grammar, the set of design rules, is transcribed into cellular automata neighborhoods as a sequenced production set that may include other simple programs (such as combinatoric instructions).(cont.) The resulting catalog of system architectures can be unmanageably large, so selection criteria (e.g., stability, matching interfaces, least action) are defined by the architect to narrow the solution space for stakeholder review. The shape grammar-cellular automata algorithmic approach was demonstrated across several domains of study. This methodology improves on the design's clarification and the number of design alternatives produced, which should result in greater stakeholder satisfaction. Of additional significance, this approach has shown value both in the study of the system architecting process, leading to the proposal of normative principles for system architecture, and in the modeling of systems for better understanding.by Thomas H. Speller, Jr.Ph.D