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The gray problem of displacement constrained topology volume minimization under multiple load cases still is an opening topic of research. A series of topologies with clear profiles generated from an optimization process are very beneficial to method engineering applications. In this paper, a novel displacement constrained optimization approach for black and white structural topology designs under multiple load cases, is proposed to obtain a series of topologies with clear profiles. Firstly, a distribution feature of constraint displacement derivatives is investigated. Secondly, an adaptive adjusting approach of design variable bounds is proposed, and an improved approximate model with varied constraint limits and a volume penalty objective function are constructed. Thirdly, an improved density-based optimization method is proposed for the displacement constrained topology volume minimization under multiple load cases. Finally, several examples are given to demonstrate that the results obtained by the proposed method provide a series of topologies with clear profiles during an optimization process. It is concluded from examples that the proposed method is effective and robust for generating an optimal topology. [ABSTRACT FROM AUTHOR]

Modern computer technology allows conjoining shakedown theory, optimization and ever stricter standardized design requirements in a single mathematical problem formulation. However it raises a question of reliability: easily achieved solution should not be taken for granted but should be adequately assessed. This paper focuses on the physical validation technique for optimal shakedown design problem solution in the aspect of Melan theorem (statics) and residual deformation compatibility (kinematics). For that purpose Rosen gradient projection method is used. Optimization problem of bending circular, symmetric plate at shakedown, which is subjected by a variable repeated load, is considered for illustration of the validation technique. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the earthquake design of reinforced concrete (RC) bridge infrastructures based on the application of multi-objective evolutionary techniques. The purpose of the methodology is to allow better decision-making for the earthquake design of bridges by proposing optimized solutions which offer trade-offs between material quantities, performance/robustness, and cost. For this, two multi-objective problems (MOPs) were defined with two sets of objectives: the first objective set optimizes the amount of material used in the piers; the second set of objectives comprises a cost function and a performance metric. The NSGA-II algorithm was adapted and applied with real coded variables and multiple non-linear dynamic analyses performed in each fitness evaluation. The results of the runs show different Pareto fronts strongly associated with solution schema of pier-deck connections and steel distribution between piers. The results also allow to perceive the influence of ductility through the impact that certain variables in certain piers have on the performance of solutions. The importance of support conditions/connections between infrastructure and deck and of confinement of piers is clear. The results of the two MOPs show that sub-optimal solutions in terms of volume of materials used may be interesting in terms of reliability gain. In the end, the results of applying the methodology present solutions which offer trade-offs and information gain valuable for bridge design. [ABSTRACT FROM AUTHOR]

Butterfly valves are a mechanical component used to regulate flow and pressure on a variety of tanks and pipeline systems. The design of this flow-control device needs to consider its structural performance as well as the flow of the fluid. In this sense, simulation and optimization tools play an important role in a butterfly valve successful development. This paper presents a global optimization of the disc of a butterfly valve by the combination of topology and shape optimization techniques. Topology optimization is employed during concept design stage to evaluate the best material distribution from a structural performance point of view. Then, based on the topology optimization results, a shape optimization, managed by Genetic Algorithms (GAs), is conducted considering structural and fluid dynamics at the same time. The results demonstrate the suitability of the proposed approach to obtain a light butterfly valve disc which satisfies the structural safety and the flow requirements. [ABSTRACT FROM AUTHOR]

A new local search algorithm for multiobjective optimization problems is proposed to find the global optima accurately and diversely. This paper models the cooperatively local search as a potential field, which is called neighborhood field model (NFM). Using NFM, a new Multiobjective Neighborhood Field Optimization (MONFO) algorithm is proposed. In MONFO, the neighborhood field can drive each individual moving towards the superior neighbor and away from the inferior neighbor. MONFO is compared with other popular multiobjective algorithms under twelve test functions. Intensive simulations show that MONFO is able to deliver promising results in the respects of accuracy and diversity, especially for multimodal problems. [ABSTRACT FROM AUTHOR]

Abstract  In this paper, a model for sharing digital pheromones between multiple particle swarms to search n-dimensional design spaces in an asynchronous parallel computing environment is presented. Particle swarm optimization (PSO) is an evolutionary technique used to effectively search multi-modal design spaces. With the aid of digital pheromones, members in a swarm can better communicate with each other to improve search performance. Previous work by the authors demonstrated the capability of digital pheromones within PSO for searching the global optimum in both single and coarse grain synchronous parallel computing environments. In the coarse grain approach, multiple swarms are simultaneously deployed across various processors and synchronization is carried out only when all swarms achieved convergence, in an effort to reduce processor-to-processor communication and network latencies. However, it is theorized that with an appropriate parallelization scheme, the benefits of digital pheromones and communication between swarms can outweigh the network bandwidth latencies resulting in improved search efficiency and accuracy. To explore this idea, a swarm is deployed in the design space across different processors. Through an additional processor, each part of the swarm can communicate with the others. While digital pheromones aid communication within a swarm, the developed parallelization model facilitates communication between multiple swarms resulting in improved search accuracy and efficiency. The development of this method and results from solving several multi-modal test problems are presented. [ABSTRACT FROM AUTHOR]

In this paper an attempt is made to develop a new Quantum Seeded Hybrid Evolutionary Computational Technique (QSHECT) that is general, flexible and efficient in solving single objective constrained optimization problems. It generates initial parents using quantum seeds. It is here that QSHECT incorporates ideas from the principles of quantum computation and integrates them in the current framework of Real Coded Evolutionary Algorithm (RCEA). It also incorporates Simulated Annealing (SA) in the selection process of Evolutionary Algorithm (EA) for child generation. The proposed algorithm has been tested on standard test problems and engineering design problems taken from the literature. In order to test this algorithm on domain-specific manufacturing problems, Neuro-Fuzzy (NF) modeling of hot extrusion is attempted and the NF model is incorporated as a fitness evaluator inside the QSHECT to form a new variant of this technique, i.e. Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Computational Technique (QSNFHECT) and is effectively applied for process optimization of hot extrusion process. The neuro-fuzzy model (NF) is also compared with statistical regression analysis (RA) model for evaluating the extrusion load. The NF model was found to be much superior. The optimal process parameters obtained by Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Technique (QSNFHECT) are validated by the finite element model. The proposed methodology using QSNFHECT is a step towards meeting the challenges posed in intelligent manufacturing systems and opens new avenues for parameter estimation and optimization and can be easily incorporated in existing manufacturing setup. [ABSTRACT FROM AUTHOR]

To reduce the computational cost of implementing computer-based simulations and analyses in engineering design, a variety of metamodeling techniques have been developed and used for the construction of metamodels. Metamodels, also called approximation models and surrogate models, can be used to make a replacement of the expensive simulation codes for design and optimization. In this paper, gene expression programming (GEP) algorithm in the evolutionary computing area is investigated as an alternative metamodeling technique to provide the approximation of a design space. The approximation performance of GEP is tested on some low-dimensional mathematical and engineering problems. A comparative study is conducted on GEP and three common metamodeling techniques in engineering design (i.e., response surface methodology (RSM), kriging and radial basis functions (RBF)) for the approximation of the low-dimensional design space. Multiple evaluation criteria are considered in the comparison: accuracy, robustness, transparency and efficiency. Two different sample sizes are adopted: small and large. Comparative results indicate that GEP can achieve the most accurate and robust approximation of a low-dimensional design space for small sample sets. For large sample sets, GEP also presents good prediction accuracy and high robustness. Moreover, the transparency of GEP is the best since it can provide clear function relationships and factor contributions by means of compact expressions. As a novel metamodeling technique, GEP shows great promise for metamodeling applications in a low-dimensional design space, especially when only a few sample points are selected and used for training. [ABSTRACT FROM AUTHOR]

The central thesis of this paper is that the dynamic performance of machinery can be improved dramatically in certain cases through a systematic and meticulous evolutionary algorithm search through the space of all structural geometries permitted by manufacturing, cost and functional constraints. This is a cheap and elegant approach in scenarios where employing active control elements is impractical for reasons of cost and complexity. From an optimization perspective the challenge lies in the efficient, yet thorough global exploration of the multi-dimensional and multi-modal design spaces often yielded by such problems. Moreover, the designs are often defined by a mixture of continuous and discrete variables-a task that evolutionary algorithms appear to be ideally suited for. In this article we discuss the specific case of the optimization of crop spraying machinery for improved uniformity of spray deposition, subject to structural weight and manufacturing constraints. Using a mixed variable evolutionary algorithm allowed us to optimize both shape and topology. Through this process we have managed to reduce the maximum roll angle of the sprayer by an order of magnitude, whilst allowing only relatively inexpensive changes to the baseline design. Further (though less dramatic) improvements were shown to be possible when we relaxed the cost constraint. We applied the same approach to the inverse problem of reducing the mass while maintaining an acceptable roll angle-a 2% improvement proved possible in this case. [ABSTRACT FROM AUTHOR]

To solve complex real-world problems, heuristics and concept-based approaches can be used to incorporate information into the problem. In this study, a concept-based approach called variable functioning (Fx) is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subsets of variables are defined with functions using information prior to optimization; thus, the function variables are optimized instead of modifying the variables in the search process. By using the problem structure analysis technique and engineering expert knowledge, the Fx method is used to enhance the steel frame design optimization process as a complex real-world problem. Herein, the proposed approach was coupled with particle swarm optimization and differential evolution algorithms then applied for three case studies. The algorithms are applied to optimize the case studies by considering the relationships among column cross-section areas. The results show that Fx can significantly improve both the convergence rate and the final design of a frame structure, even if it is only used for seeding. [ABSTRACT FROM AUTHOR]

Design methodologies of today require the solution of several many-objective optimization problems. The last two decades have seen a surge in several algorithms capable of solving multi-objective optimization problems. It was only in the past 5 years that new algorithms capable of coping with a large number of objectives have been introduced. This work presents a new differential evolution algorithm (NSDE-R) capable of efficiently solving many-objective optimization problems. The algorithms make use of reference points evenly distributed through the objective function space to preserve diversity and aid in multi-criteria-decision-making. The proposed NSDE-R was applied to test problems from the DTLZ and WFG suite, having three to 15 objectives. Two mutation donor operators were investigated for their ability to converge to the analytical Pareto front while maintaining diversity. The ability of NSDE-R to converge to a user-specified region of the Pareto front is also investigated. The proposed NSDE-R algorithm has shown to have a higher rate of convergence and better convergence to the analytical Pareto front. [ABSTRACT FROM AUTHOR]

In this study, the multi-objective optimal design of hybrid viscoelastic/composite sandwich beams for minimum weight and minimum vibration response is aimed. The equation of motion for linear vibrations of a multi-layer beam is derived by using the principle of virtual work in the most general form. These governing equations together with the boundary conditions are discretized by the generalized differential quadrature method (GDQM) in the frequency domain for the first time. Also, the time and temperature dependent properties of the viscoelastic materials are taken into consideration by a novel ten-parameter fractional derivative model that can realistically capture the response of these materials. The material variability is accounted for by letting an optimization algorithm choose a material freely out of four fiber-reinforced composite materials and five viscoelastic damping polymers for each layer. The design parameters, i.e., the orientation angles of the composites, layer thicknesses and the layer materials that give the set of optimal solutions, namely the Pareto frontier, is obtained for the three and nine-layered clamped-free sandwich beams by using a variant of the non-dominated sorting genetic algorithms (NSGA II). [ABSTRACT FROM AUTHOR]

We propose an automated shape generative framework, which provides an alternative way of exploring the design space in a structural mechanics context. The framework presented uses 'blind' evolutionary intelligence to synthesise shape grammar sentences i.e. Grammatical Evolution (GE), where rules are selected by a Genetic Algorithm (GA). This is a novel approach to automate the Shape Grammar (SG) formalism. We then present an application of a grammar based shape generative framework to solve a 2D design optimisation problem. This involves synthesis of parametric 2D curves where the shape grammar primitives are introduced as arcs represented by rotation and a radius. The efficacy of the proposed shape generative framework is then compared with that of Non-Uniform Rational B-Splines (NURBS) parametrisation for structural optimisation. [ABSTRACT FROM AUTHOR]

The design space of multi-stage transmissions is usually very large and heavily constrained. This places significant demands on the algorithm employed to search it, but successful optimization has the potential to yield considerably better designs than conventional heuristics, at the same time enabling a better understanding of the trade-offs between various objectives (such as service life and overall weight). Here we tackle a two-stage helical gear transmission design problem (complete with the sizing and selection of shafts, bearings, housing, etc.) using a two-phase evolutionary algorithm in a formulation that can be extended to include additional stages or different layouts. [ABSTRACT FROM AUTHOR]

Abstract  Compliant mechanisms are designed to be intentionally flexible, providing hingeless mechanisms. This work contributes a complex-shaped beam element formulation in conjunction with the ground structure approach. We identify compliant mechanism design solutions by using evolutionary topology optimization and increase flexibility by using a parametrization concept based on graph theory. The new operators for evolutionary optimization are also explained and sample problems are used to address the question of how our contribution increases design solutions space. [ABSTRACT FROM AUTHOR]