Engineering design using genetic algorithms

As modern computational and modeling technologies grow, engineering design heavily relies on computer modeling and simulation to accelerate design cycles and save cost. A complex design problem will involve many design parameters and tables. Exploring design space and finding optimal solutions are still major challenges for complex systems. This dissertation proposed to use Genetic Algorithms to optimize engineering design problems. It proposed a software infrastructure to combine engineering modeling with Genetic algorithms and covered several aspects in engineering design problems. The dissertation suggested a new Genetic Algorithm (Completely dominant Genetic algorithm) to quickly identify High Performance Areas for Engineering Design. To help design engineers to explore design space, the dissertation used a new visualization tool to demonstrate high dimensional Genetic Algorithm results in dynamical graphics. Robustness of design is critical for some of the engineering design applications due to perturbation and manufacturing tolerance. This dissertation demonstrated to use Genetic Algorithms to locate robust design areas and provided a thorough discussion on robustness and diversity in depth

Visualizing engineering design data using a modified two-level self-organizing map clustering approach

Engineers tasked with designing large and complex systems are continually in need of decision-making aids able to sift through enormous amounts of data produced through simulation and experimentation. Understanding these systems often requires visualizing multidimensional design data. Visual cues such as size, color, and symbols are often used to denote specific variables (dimensions) as well as characteristics of the data. However, these cues are unable to effectively convey information attributed to a system containing more than three dimensions. Two general techniques can be employed to reduce the complexity of information presented to an engineer: dimension reduction, and individual variable comparison. Each approach can provide a comprehensible visualization of the resulting design space, which is vital for an engineer to decide upon an appropriate optimization algorithm. Visualization techniques, such as self-organizing maps (SOMs), offer powerful methods able to surmount the difficulties of reducing the complexity of n-dimensional data by producing simple to understand visual representations that quickly highlight trends to support decision-making. The SOM can be extended by providing relevant output information in the form of contextual labels. Furthermore, these contextual labels can be leveraged to visualize a set of output maps containing statistical evaluations of each node residing within a trained SOM. These maps give a designer a visual context to the data set’s natural topology by highlighting the nodal performance amongst the maps. A drawback to using SOMs is the clustering of promising points with predominately less desirable data. Similar data groupings can be revealed from the trained output maps using visualization techniques such as the SOM, but these are not inherently cluster analysis methods. Cluster analysis is an approach able to assimilate similar data objects into “natural groups” from an otherwise unknown prior knowledge of a data set. Engineering data composed of design alternatives with associated variable parameters often contain data objects with unknown classification labels. Consequently, identifying the correct classifications can be difficult and costly. This thesis applies a cluster analysis technique to SOMs to segment a high-dimensional dataset into “meta-clusters”. Furthermore, the thesis will describe the algorithm created to establish these meta-clusters through the development of several computational metrics involving intra and inter cluster densities. The results from this work show the presented algorithm’s ability to narrow a large-complex system’s plethora of design alternatives into a few overarching set of design groups containing similar principal characteristics, which saves the time a designer would otherwise spend analyzing numerous design alternatives

Peeking beyond peaks:Challenges and research potentials of continuous multimodal multi-objective optimization

Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem's landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.</p

A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions

Manufacturing advanced materials and products with a specific property or combination of properties is often warranted. To achieve that it is crucial to find out the optimum recipe or processing conditions that can generate the ideal combination of these properties. Most of the time, a sufficient number of experiments are needed to generate a Pareto front. However, manufacturing experiments are usually costly and even conducting a single experiment can be a time-consuming process. So, it's critical to determine the optimal location for data collection to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This paper presents a novel data-driven Bayesian optimization framework that utilizes sequential learning to efficiently optimize complex systems with multiple conflicting objectives. Additionally, this paper proposes a novel metric for evaluating multi-objective data-driven optimization approaches. This metric considers both the quality of the Pareto front and the amount of data used to generate it. The proposed framework is particularly beneficial in practical applications where acquiring data can be expensive and resource intensive. To demonstrate the effectiveness of the proposed algorithm and metric, the algorithm is evaluated on a manufacturing dataset. The results indicate that the proposed algorithm can achieve the actual Pareto front while processing significantly less data. It implies that the proposed data-driven framework can lead to similar manufacturing decisions with reduced costs and time

Explicit Building-Block Multiobjective Genetic Algorithms: Theory, Analysis, and Developing

This dissertation research emphasizes explicit Building Block (BB) based MO EAs performance and detailed symbolic representation. An explicit BB-based MOEA for solving constrained and real-world MOPs is developed the Multiobjective Messy Genetic Algorithm II (MOMGA-II) which is designed to validate symbolic BB concepts. The MOMGA-II demonstrates that explicit BB-based MOEAs provide insight into solving difficult MOPs that is generally not realized through the use of implicit BB-based MOEA approaches. This insight is necessary to increase the effectiveness of all MOEA approaches. In order to increase MOEA computational efficiency parallelization of MOEAs is addressed. Communications between processors in a parallel MOEA implementation is extremely important, hence innovative migration and replacement schemes for use in parallel MOEAs are detailed and tested. These parallel concepts support the development of the first explicit BB-based parallel MOEA the pMOMGA-II. MOEA theory is also advanced through the derivation of the first MOEA population sizing theory. The multiobjective population sizing theory presented derives the MOEA population size necessary in order to achieve good results within a specified level of confidence. Just as in the single objective approach the MOEA population sizing theory presents a very conservative sizing estimate. Validated results illustrate insight into building block phenomena good efficiency excellent effectiveness and motivation for future research in the area of explicit BB-based MOEAs. Thus the generic results of this research effort have applicability that aid in solving many different MOPs

Polymer single screw extruder optimization using tchebycheff scalarization method and simulated annealing algorithm

The single screw extrusion optimal design involves the optimization of six criteria that can be efficiently handled by a weighted Tchebycheff scalarization method. The performance of the method has been analyzed for three different methods to generate weight vectors. The experimental results show that the tested strategies provide similar and reasonable solutions and supply a valuable procedure to identify good trade-offs between conflicting objectives.European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 734205- H2020-MSCA-RISE-2017. The work has also been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, UIDB/00013/2020 and UIDP/00013/2020 of CMAT-U

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

Computer network security is a very serious concern in many commercial, industrial, and military environments. This paper proposes a new computer network security approach defined by self-organized agent swarms (SOMAS) which provides a novel computer network security management framework based upon desired overall system behaviors. The SOMAS structure evolves based upon the partially observable Markov decision process (POMDP) formal model and the more complex Interactive-POMDP and Decentralized-POMDP models, which are augmented with a new F(*-POMDP) model. Example swarm specific and network based behaviors are formalized and simulated. This paper illustrates through various statistical testing techniques, the significance of this proposed SOMAS architecture, and the effectiveness of self-organization and entangled hierarchies