Immune network modeling in multicriterion design of structural systems

Space satellite design generally involves multidisciplinary collaboration processes, such as structural, heat transfer and electrical design processes, aimed at fulfilling various functional requirements that are subject to stringent weight and spatial limitations. This paper discusses a practical application of a systematic design optimization approach to the design of a small satellite called “Micro-LabSat,” in order to obtain optimal solutions that take into account such multidisciplinary design factors. The satellite design problem has two sub design problems, a component arrangement problem and a structural design problem, and the features of these two problems are entirely different since the component arrangement problem involves discrete design variables such as the positions of the components, while the structural design problem has continuous design variables such as the dimensions of the parts. Therefore, a bi-level optimization procedure is applied. First, two sublevel optimization problems for the sub design problems are solved. A two-phase optimization procedure that uses genetic algorithms and SQP (Sequential Quadratic Programming) is applied to the component arrangement problem, and SQP is applied to the structural optimization. Then, the satellite-level optimization problem that considers all factors, including both the component arrangement and structural problems, is solved using the sublevel optimal solutions as initial points for optimization. The bi-level optimization scheme presented here provides an efficient search procedure, and offers optimal solutions in short calculation times.

The integrated optimization of structure and control systems is investigated for 2-D single structure using a typical planar frame taken from a building. The vibration control model is established for the plane frame, and the LQR algorithm is used to obtain equivalent stiffness and damping parameters of assigned passive controllers. The integrated optimization model, including design variables, the objective function and constraint function, is built. The design variables are the size parameters of structural elements, the parameters of the weighted matrix, the number and locations of the controllers. The maximal displacement of the controlled system in time domain is introduced as the objective function. Genetic algorithm is adopted to solve such a kind of optimization problem with discrete and continuous design variables. The results of a numerical example show that the proposed method is reasonable and effective for the integrated design of control/structural systems subjected to seismic load.

최근에는 고층구조물이나 해양구조물(플랜트)의 경우 대부분 대형화로 설계 및 시공되고 있다. 대형구조물의 경우 주로 강재(steel)를 사용하여 설계하고 있다. 구조물 설계는 설계단계에서 최상의 부재를 선택하는 최적화를 적용함으로서 비용을 줄이는 경제적인 효과를 얻을 수 있다. 본 연구에서는 트러스와 프래임 구조물의 최적설계에 연속변수와 이산변수를 사용하여 최적화 하였고, 설계에서 사용된 이산자료는 규격화된 강재에 대한 자료와 데이터화된 이산자료를 사용하였다. 최적화는 이 모두에 적합한 이산최적화를 적용하였다. 최적화 기법으로는 유전자 알고리즘을 사용하였으며, 사용된 구조물은 10-Bar, 25-Bar 트러스와 1경간 2층, 1경간 7층 프레임 구조물이다. 설계시 제약조건은 고유진동수와 부재응력, 변위제약조건들이 적용되었다. 유전자 알고리즘에는 번식과장에서 엘리트 개체가 2번 사용되도록 하였으며, 제약조건 위반시 패널티를 사용하는 방법을 사용하였다. We present optimum design for truss and frame structures subject to constraints on stresses, displacement, and natural frequency. The optimum design procedure is used discrete and continuous design variables and Genetic Algorithms. Genetic Algorithms is used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process, and optimum design is used steel(W-section) and pre-made discrete cross-section. Truss and frame structures optimization examples are used for 10-Bar truss, 25-Bar truss, 1-bay 2-story frame, 1-bay 7-story frame, and these examples are employed to demonstrate the availability and serviceability of Genetic Algorithms for solving optimum design of truss and frame.

Mechanical sizing of a composite launcher structure by hybridizing a genetic algorithm with a local search method

Refined models for the optimal design of adaptive structures using simulated annealing

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed modification improves the efficiency of the memory constructed in terms of the continuous variables. The work presents the algorithmic implementation of the proposed memory scheme and demonstrates the efficiency of the proposed multivariate approximation procedure for the weight optimization of a segmented open cross section composite beam subjected to axial tension load. Results are generated to demonstrate the advantages of the proposed improvements to a standard genetic algorithm.

Purpose – The structural design problem can be viewed as an iterative design loop with each iteration involving two stages for topology and shape designs with genetic algorithm (GA) as the optimization tool for both.

Rendezvous phasing special-point maneuvers mixed discrete-continuous optimization using simulated annealing

The species conservation technique described here, in which the population of a genetic algorithm is divided into several groups according to their similarity, is inspired by ecology. Each group with similar characteristics is called a species and is centred on a dominating individual, called the species seed. A genetic algorithm based on this species conservation technique, called the species-conserving genetic algorithm (SCGA), was established and has been proved to be effective in finding multiple solutions of multimodal optimization problems. In this article, the SCGA is used to solve engineering design optimization problems. Different distance measures (measures of similarity) are investigated to analyse the performance of the SCGA. It is shown that the Euclidean distance is not the only possible basis for defining a species and sometimes may not make sense in engineering applications. Two structural design problems are used to demonstrate how the choice of a meaningful measure of similarity will help the exploration for significant designs.

In this paper, genetic algorithm (GA) is applied to the optimum design of reinforced concrete liquid retaining structures, which comprise three discrete design variables, including slab thickness, reinforcement diameter and reinforcement spacing. GA, being a search technique based on the mechanics of natural genetics, couples a Darwinian survival-of-the-fittest principle with a random yet structured information exchange amongst a population of artificial chromosomes. As a first step, a penalty-based strategy is entailed to transform the constrained design problem into an unconstrained problem, which is appropriate for GA application. A numerical example is then used to demonstrate strength and capability of the GA in this domain problem. It is shown that, only after the exploration of a minute portion of the search space, near-optimal solutions are obtained at an extremely converging speed. The method can be extended to application of even more complex optimization problems in other domains.

Introduction I N the last years genetic algorithms (GAs) have received much attention as a powerful design tool in many areas of aerospace engineering. These probabilistic-typemethods, based on the principle of the natural evolution, have shown their effectiveness and robustness in a wide range of optimizationproblems. i 5 Compared with classical deterministic approaches, GAs’ superior effectiveness in performing optimization tasks is mainly from some speciŽ c features such as the capability to handle simultaneously continuous integer and discrete design variables, a parallel-like searching method leadingto a greatereffectivenessin Ž ndingglobalminimum within the design space. Moreover, as genetic-type optimization is guided only by a function of merit (or Ž tness) value, no mathematical informationon the objective function is required.Therefore function discontinuitiescan be easily managed. In this Note an application in the Ž eld of aircraft conceptual design is described. A genetic optimizer has been coupled with a sizing code to deŽ ne a short/medium range preliminary aircraft conŽ guration,powered by turbofan engines, fully compliant with given requirements and allowingminimumdirect operatingcost. Parametric formulas6 i 8 have been used for aerodynamics, weights, and low-speed performance calculation.Rationale for GA application in conceptualdesignwith reference to selection and sizing is well presented in Ref. 9. Other relevantexamplesof geneticapproachin aircraftdesignare reported in Refs. 10–13.

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Back et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint 27

One of the main problems in modern mathematical modeling is to obtain high-precision solutions of boundary value problems. This study proposes a new approach that combines the methods of artificial intelligence and a classical analytical method. The use of the analytical method of fictitious canonic regions is proposed as the basis for obtaining reliable solutions of boundary value problems. The novelty of the approach is in the application of artificial intelligence methods, namely, genetic algorithms, to select the optimal location of fictitious canonic regions, ensuring maximum accuracy. A general genetic algorithm has been developed to solve the problem of determining the global minimum for the choice and location of fictitious canonic regions. For this genetic algorithm, several variants of the function of crossing individuals and mutations are proposed. The approach is applied to solve two test boundary value problems: the stationary heat conduction problem and the elasticity theory problem. The results of solving problems showed the effectiveness of the proposed approach. It took no more than a hundred generations to achieve high precision solutions in the work of the genetic algorithm. Moreover, the error in solving the stationary heat conduction problem was so insignificant that this solution can be considered as precise. Thus, the study showed that the proposed approach, combining the analytical method of fictitious canonic regions and the use of genetic optimization algorithms, allows solving complex boundary-value problems with high accuracy. This approach can be used in mathematical modeling of structures for responsible purposes, where the accuracy and reliability of the results is the main criterion for evaluating the solution. Further development of this approach will make it possible to solve with high accuracy of more complicated 3D problems, as well as problems of other types, for example, thermal elasticity, which are of great importance in the design of engineering structures.

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

The challenge of determining the best design in a multimodal design space with multiple local optimal solutions often challenges the best available optimization techniques. By casting the objective function of the optimization problem in the form of a Non-Uniform Rational B-spline (NURBs) metamodel, known as a HyPerModel, significant optimization advantages can be achieved, including the ability to efficiently find the global metamodel optimum solution with less computational expense than traditional approaches. This optimization strategy, defined by the HyPerOp algorithm, uses the underlying structure of a HyPerModel to intelligently select starting points for optimization runs and to identify regions of the design space that do not contain locations for the global metamodel optimum location. This paper describes the application of the HyPerOp algorithm to mixed integer programming problems and demonstrates its use with two example applications. The algorithm works with design spaces composed of continuous and integer design variables and provides a complementary approach for improved optimization capabilities.