Evolutionary Design Optimization Research Articles

Evolutionary seismic design optimization of mega‐brace damper architectures in three‐dimensional structures

AbstractNovel seismic protection systems are widely used to improve building performance or collapse prevention under extreme events. However, a systematic approach for the distribution of dampers in structures expected to operate inelastically under extreme seismic loading does not exist in modern seismic codes. Furthermore, optimal designs are achieved typically performing only forward analysis with no direction on how device topologies and properties distribution affect the response and the plastic action distribution throughout structures for different types of passive devices. To address the above, this study utilizes an evolutionary framework for optimization of the topology and properties of passive dampers in three‐dimensional buildings subject to seismic loading. The framework incorporates a self‐organizing evolutionary algorithm, a novel concept of mega‐brace damper architecture scheme, and an active ground motion subset scheme to efficiently find optimal designs that satisfy predefined performance targets. Three‐dimensional buildings considered include eight‐story irregular, eight‐story regular, and 14‐story regular structures, while passive devices included yielding, friction, and viscous dampers. The seismic environment consisted of twenty‐five synthetic ground motions with 5% of probability of exceedance in 50 years. The study included design examples with realistic three‐dimensional buildings, and the proposed framework yielded optimal designs that exhibited uniform ductility demand along the height when compared to the base buildings. Novel three‐dimensional multiscale mega‐brace architectures not seen in practice were identified, while their corresponding plastic action distribution throughout the buildings was assessed for each case.

A nodal‐based evolutionary optimization algorithm for frame structures

AbstractThis work proposes a nodal‐based evolutionary design optimization algorithm to design frame structures whose edges are the Delaunay triangulation of homogeneously distributed nodes in the design domain. The remaining nodes can freely sway in the design domain except for the loading nodes and boundary nodes. As a result, it can extend the space of admissible solutions to this optimization problem and reduce the number of design variables. Then, the sensitivity of the objective function, namely, the sum of compliance and its volume, is input into the method of moving asymptotes to update the nodal coordinates and member thickness. The most inefficient node is deleted in each iteration based on the average nodal strain energy until its number reaches a prescribed limit. 2D numerical examples for the Michell arch, L‐shaped bracket, and Messerschmidt–Bölkow–Blohm beam show that the proposed algorithm can get the optimal structure within a few iterations. Compared with initial configurations in 3D, the optimal crane arm, transmission tower, and aquatic dome have less strain energy and fewer materials, showing a great potential of the proposed method to design actual space frames.

Entropy maximizing evolutionary design optimization of water distribution networks under multiple operating conditions

The informational entropy model for flow networks was formulated over 30 years ago by Tanyimboh and Templeman (University of Liverpool, UK) for a single discrete operating condition that typically comprises the maximum daily demands and was undefined for water distribution networks (WDNs) under multiple operating conditions. Its extension to include multiple independent discrete operating conditions was investigated experimentally herein considering the relationships between flow entropy and hydraulic capacity reliability and redundancy. A novel penalty-free multi-objective genetic algorithm was developed to minimize the initial construction cost and maximize the flow entropy subject to the design constraints. Furthermore, optimized designs derived from the maximum daily demands as a single discrete operating condition were compared to those derived from a combination of discrete operating conditions. Optimized designs from a combination of discrete operating conditions outperformed those from a single operating condition in terms of performance and initial construction cost. The best results overall were achieved by maximizing the sum of the flow entropies of the discrete operating conditions. The logical inference from the results is that the flow entropy of multiple discrete operating conditions is the sum of their respective entropies. In addition, a crucial property of the resulting flow entropy model is that it is bias free with respect to the individual operating conditions; hitherto a fundamental weakness concerning the practical application of the flow entropy model to WDNs is thus addressed.

Hypothesis derivation and its verification by a wholly automated many-objective evolutionary optimization system

This study has constructed a fully automated multidisciplinary and many-objective evolutionary design optimization system independent of computer environments to evaluate objective functions; the research applied it to a geometric design problem of a flyback booster for next-generation space transportation. In optimization involving objective functions to appraise the aero-/structural-dynamic performance with high fidelity, spatial discretization hinders the overall automation. This research has facilitated an efficient optimal design by wholly automating high-fidelity assessments, which designers had to implement manually, and has accomplished optimizations that directly contribute to real-world design problems. Moreover, this study would accumulate design knowledge for space transportation that the market is reviving. The total automated system yielded the embedding of geometric trait lines to ensure the discretization even for large curvature surfaces; the system innovated a robust automatic error-checking mechanism in the system’s preprocess. Consequently, the entirely automatized optimization procured nondominated solution sets for more precise data analyses in a pragmatic execution period. Design informatics, a framework combining optimization and data analysis, functioned usefully in real-world design on flyback-booster geometry by materializing smooth deriving and verifying a design hypothesis; eventually, the research gained a new design principle.

Evolutionary Design Optimization of an Alkaline Water Electrolysis Cell for Hydrogen Production

Hydrogen is an excellent energy source for long-term storage and free of greenhouse gases. However, its high production cost remains an obstacle to its advancement. The two main parameters contributing to the high cost include the cost of electricity and the cost of initial financial investment. It is possible to reduce the latter by the optimization of system design and operation conditions, allowing the reduction of the cell voltage. Because the CAPEX (initial cost divided by total hydrogen production of the electrolyzer) decreases according to current density but the OPEX (operating cost depending on the cell voltage) increases depending on the current density, there exists an optimal current density. In this paper, a genetic algorithm has been developed to find the optimal evolution parameters and to determine an optimum electrolyzer design. The optimal current density has been increased by 10% and the hydrogen cost has been decreased by 1%.

Efficient Surrogate-Based NVH Optimization of a Full Vehicle Using FRF Based Substructuring

<div class="section abstract"><div class="htmlview paragraph">The computer simulation with the Finite Element (FE) code for the structural dynamics becomes more attractive in the industry. However, it normally takes a prohibitive amount of computation time when design optimization is performed with running a large-scale FE simulation many times. Exploiting Dynamic Structuring (DS) leads to alleviating the computational complexity since DS necessities iterative reanalysis of only the substructure(s) to be optimally designed. In this research, Frequency Response Function (FRF) based substructuring is implemented to realize the benefits of DS for fast single- and multi-objective evolutionary design optimization. Also, Differential Evolution (DE) is first combined with two sorting approaches of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Infeasibility Driven Evolutionary Algorithm (IDEA) for effective constrained single- and multi-objective evolutionary optimization. The effectiveness of the proposed algorithm (NSGA-II/DE-IDEA) is verified using several test functions for constrained single- and multi-objective optimization. To circumvent the need for frequent time-consuming simulation runs, Kriging surrogate models are established by interpolating the responses simulated at the sample points, which are generated by executing an Optimal LHS algorithm. Besides, the Morris method is implemented to leave out unimportant design variables. A constrained single-objective and a constrained multi-objective NVH design optimization of a truck are carried out to demonstrate the surrogate-based design optimization process involving FRF based substructuring and the proposed algorithm.</div></div>

Evolutionary algorithm-based design optimization for right choice of transformer conductor material and stepped core

In this paper two crucial variables, namely type of winding material and number of core steps, have been proposed to minimize the purchase cost, total mass, loss, and life time cost of the transformer. In order to make cost-effective and energy-efficient transformer design, the proposed variables are used in conjunction with six primary variables for deciding the most appropriate conductor and core step size. Further, the efficacy of the proposed methodology, in terms of cost savings, material savings, and loss reduction, is tested on 1500-kVA transformer using covariance matrix adaptation evolution strategy (CMA-ES) and self-adaptive differential evolution (SaDE). It is revealed that the proposed crucial variables with CMA-ES provide better design option than the SaDE and knowledge-based systems. Also, faster convergence and consistency have been established with CMA-ES than SaDE through the statistical analysis. Core losses are calculated using proposed methodology, and employing analytical equations is validated using 3D finite element method.

Self-Adaptive Solution-Space Reduction Algorithm for Multi-Objective Evolutionary Design Optimization of Water Distribution Networks

An effective way to improve the computational efficiency of evolutionary algorithms is to make the solution space of the optimization problem under consideration smaller. A new reliability-based algorithm that does this was developed for water distribution networks. The objectives considered in the formulation of the optimization problem were minimization of the initial construction cost and maximization of the flow entropy as a resilience surrogate. After achieving feasible solutions, the active solution space of the optimization problem was re-set for each pipe in each generation until the end of the optimization. The algorithm re-sets the active solution space by reducing the number of pipe diameter options for each pipe, based on the most likely flow distribution. The main components of the methodology include an optimizer, a hydraulic simulator and an algorithm that calculates the flow entropy for any given network configuration. The methodology developed is generic and self-adaptive, and prior setting of the reduced solution space is not required. A benchmark network in the literature was investigated, and the results showed that the algorithm improved the computational efficiency and quality of the solutions achieved by a considerable margin.

Evolutionary design optimization of MEMS: a review of its history and state-of-the-art

As an important part of the internet of things (IoTs) and cyber-physical systems (CPS), Micro-Electro-Mechanical-Systems (MEMS) is playing more and more irreplaceable role in current industrial community and the forthcoming era of the Industry 4.0. The limitations of some frequently used design methods for MEMS design optimization are analyzed in this review. In order to overcome these difficulties, a recent trend in design optimization of MEMS is inspired by the natural evolution mechanism. Many powerful techniques, especially the evolutionary computation (EC), have been used for the design optimization of MEMS. This paper presents a review of the achievements in this promising research area which utilizes EC methods for the design optimization of MEMS and also proposes three open issues that it is facing.

Evolutionary design optimization of traffic signals applied to Quito city.

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.

Multi-Directional Maximum-Entropy Approach to the Evolutionary Design Optimization of Water Distribution Systems

A new multi-directional search approach that aims at maximizing the flow entropy of water distribution systems is investigated. The aim is to develop an efficient and practical maximum entropy based approach. The resulting optimization problem has four objectives, and the merits of objective reduction in the computational solution of the problem are investigated also. The relationship between statistical flow entropy and hydraulic reliability/failure tolerance is not monotonic. Consequently, a large number of maximum flow entropy solutions must be investigated to strike a balance between cost and hydraulic reliability. A multi-objective evolutionary optimization model is developed that generates simultaneously a wide range of maximum entropy values along with clusters of maximum and near-maximum entropy solutions. Results for a benchmark network and a real network in the literature are included that demonstrate the effectiveness of the procedure.

Design evolution of engineering systems using bond graphs and genetic programming

This paper presents a scheme of evolutionary design optimization, which integrates modeling with bond graphs and optimization using genetic programming for multi-domain engineering systems, particularly mechatronic systems. The performance of the developed system is studied using both experimentation and simulation. During the evolutionary optimization, in addition to the desired response error, system complexity is also taken into account. For the experimental study, the method is implemented in an industrial fish processing machine at the Industrial Automation Laboratory of the University of British Columbia, and the obtained results for suggested design modifications are studied and tested. The drawbacks of the fitness calculation methodologies that are presented in literature are identified and improved fitness functions are developed for evolutionary design in the present work. While previous work has investigated the integration of bond graphs and genetic programming for designing an engineering system, the present work specifically addresses the application of the developed method for the design improvement of an industrial machine. The proposed method is applicable particularly to existing engineering systems, first because the initial model can be tested by comparing its simulated results with the corresponding results from the actual physical system, and second because the design improvements as suggested by the evolutionary design framework, which is developed in the present work, may be implemented and tested against the behavior of the corresponding model.

A survey on automating configuration and parameterization in evolutionary design exploration

AbstractConfiguration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.

Integrated multifidelity, multidisciplinary evolutionary design optimization of counterrotating compressors

In high-speed air-breathing propulsion, compact turbomachinery is essential to reduce the engine size and weight. Hence, high compression ratios are required in the compressor stages. This paper proposes an optimization-based integrated design approa

Evolutionary forging preform design optimization using strain-based criterion

Preform design plays an important role in forging design especially for parts with complex shapes. In this paper, an attempt was made to develop a topological optimization approach for the preform design in bulk metal forming processes based on the bidirectional evolutionary structural optimization strategy. In this approach, a new strain-based element addition and removal criterion has been proposed for evaluating and optimizing the material flow in the forging process. To obtain a smooth preform boundary, a closed B-spline curve based on the least square algorithm is employed to approximate the uneven surface of the updated preform profile. A C# program has been developed to integrate the FE simulation, shape optimization, and surface approximation processes. Two 2D forging preform design problems are evaluated by using the developed method. The results suggest that the optimized preform with the strain uniformity criterion has shown better performance in improving the material flow and deformation uniformity during the forging process. The results also demonstrate the robustness and efficiency of the developed preform optimization method.

Evolvable Embryonics: 2-in-1 Approach to Self-healing Systems

This paper covers the authors’ recent research in the area of evolutionary design optimisation in electronic application domain (Evolvable Hardware). This will be also presented in the context of biologically inspired systems where Evolvable Hardware is concerned with evolutionary synthesis of self-healing systems and potentially hardware capable of online adaptation to dynamically changing environment. We will also illustrate how EAs can produce novel and unintuitive design solutions, and possibly new design principles. The novelty of this research project addresses this compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired mechanism to support the design optimisation of self-healing architectures, that is Evolvable-Embryonics.

A genotype-phenotype-fitness assessment protocol for evolutionary self-assembly Wang tiles design

In a previous work we have reported on the evolutionary design optimisation of self-assembling Wang tiles capable of arranging themselves together into a target structure. Apart from the significant findings on how self-assembly is achieved, nothing has been yet said about the efficiency by which individuals were evolved. Specially in light that the mapping from genotype to phenotype and from this to fitness is clearly a complex, stochastic and non-linear relationship. One of the most common procedures would suggest running many experiments for different configurations followed by a fitness comparison, which is not only time-consuming but also inaccurate for such intricate mappings. In this paper we aim to report on a complementary dual assessment protocol to analyse whether our genetic algorithm, using morphological image analyses as fitness function, is an effective methodology. Thus, we present here fitness distance correlation to measure how effectively the fitness of an individual correlates to its genotypic distance to a known optimum, and introduce clustering as a mechanism to verify how the objective function can effectively differentiate between dissimilar phenotypes and classify similar ones for the purpose of selection.

Hybrid evolutionary optimal MEMS design

A hybrid evolutionary design synthesis and optimization process for microelectromechanical systems (MEMS) devices has been developed. The process integrates a MEMS design component library with multiple simulation modules and two levels of design optimization: global multi-objective genetic algorithms (MOGA) and local gradient-based refinement. During the hybrid evolutionary design process, MOGA randomly searches the design space and approaches the desirable design solutions using probabilistic transition rules, and gradient-based local optimization refines promising design candidates with computational efficiency. To efficiently apply hybrid evolutionary optimization techniques on MEMS designs, a hierarchical tree-structured component-based genotype representation has been developed, which incorporates specific engineering knowledge into the design synthesis and optimization process. The MEMS design component library serves as a source of practical and efficient genotypes for the evolutionary process, with each component associated with its instructions and restrictions on genetic operations. The component-based genotype incorporated with engineering knowledge constrains evolutionary searching in appropriate and promising regions of the search space, allowing a deeper search in a given amount of time. Hybrid evolutionary MEMS design synthesis and optimization are demonstrated with surface-micromachined resonator and accelerometer designs.

Guest Editorial: Special Issue on Nature Inspired Cooperative Strategies for Optimization (Part I)

Nature is an unlimited source of resilient, robust, fine tuned,complex mechanisms and phenomena which have been thesubject of exploration and exploitation in science and engi-neering. In particular, understanding complex cooperativesystems in nature has driven computer scientists and prac-titionerstooutstandingdevelopmentsrangingfromversatilemodels and architectures to highly effective methodologiesand computing strategies. Examples of these include, butare not limited to, artificial bee colony, memetic-computingagent-models, bacteria metabolism inspired robot control-lers, evolutionary design optimization, particle swarm opti-mization, emergent collective robotics behavior to name butafew.This thematic issue brings a selection of the best researchworkspublishedintheIVInternationalWorkshoponNatureInspired Cooperative Strategies for Optimization (NICSO)held on May 2010 in Granada, Spain. The aim of NICSO istobringtogetherscientistsfromallovertheworldtodiscussthe latest ideas and state of the art on nature inspired coop-erative strategies. As in its previous editions, NICSO 2010hascoveredtopicsrelatedtoadaptivebehavior,antcolonies,amorphous computing, artificial life, bio-inspired architec-tures, distributed computing, evolutionary robotics, evolv-ablesystems,membranecomputing,softwareself-assembly,evolutionary computation, swarm intelligence and quantum

Phi-array: A novel method for fitness visualization and decision making in evolutionary design optimization

There is a growing interest in integrating model based evolutionary optimization in engineering design decision making for effective search of the solution space. Most applications of evolutionary optimization are concerned with the search for optimal solutions satisfying pre-defined constraints while minimizing or maximizing desired goals. A few have explored post-optimization decision making using concepts such as Pareto optimality, but mostly in multi-objective problems. Sub-optimal solutions are usually discarded and do not contribute to decision making after optimization runs. However, the discarded ‘inferior’ solutions and their fitness contain useful information about underlying sensitivities of the system and can play an important role in creative decision making. The need for understanding the underlying system behavior is more pronounced in cases where variations in the genotype space can cause non-deterministic changes in either the fitness or phenotype space and where fitness evaluations are computationally expensive. The optimized design of an artificial lighting environment of a senior living room is used as a test case to demonstrate the need for and application of fitness visualization in genotype and phenotype spaces for effective decision making. Sub-optimal solutions are retained during optimization and visualized along with the optimum solution in a fitness array visualization system called phi-array, developed as part of this research. The optimization environment is based on genetic algorithm (GA) in which a compute-intensive raytracing rendering engine, RADIANCE, is used to evaluate the fitness of prospective design solutions. Apart from describing the development of the optimization system and demonstrating the utility of phi-array in effective decision making, this article explores optimization parameters and their effectiveness for artificial lighting design problems and the nature of their rugged fitness and constraint landscapes.