Modified simulated annealing algorithm for optimal design of steel structures

Structural optimization aims to design structures under certain constraints to achieve better behavior and have a proper manufacturing cost. This type of optimization corresponds to highly non-linear and non-convex problems including several local optima. Therefore, to solve such problems effectively, designers need to use adequate optimization methods which can make a good balance between the computational cost and the quality of solutions. In this paper the modified simulated annealing algorithm (MSAA) is employed to solve optimal design of steel structures. MSSA is a newly improved version of the simulated annealing (SA) algorithm with three modifications: preliminary exploration, search step and a new probability of acceptance. The performance, robustness and applicability of the MSAA are demonstrated through six structural optimization problems. Obtained results in all considered examples indicate that the MSAA is superior to several other methods in existing literature in terms of the quality of solution and convergence speed.Peer Reviewe

Imperialist Competitive Algorithm with Independence and Constrained Assimilation

Autonomous Supply Chai

Optimal Design of Steel Structures Using Innovative Black Widow Algorithm Hybridized with Greedy Sensitivity-Based Particle Swarm Optimization Technique

This paper presents a Greedy Sensitivity-based analysis implemented on the Particle Swarm Optimization search engine (GSPSO). The effectiveness of the method focuses mainly on providing an intelligent population to enter meta-heuristic algorithms. As a meta-heuristic method in the second stage, the recently introduced Black Widow Optimization (BWO) algorithm was selected and improved by the authors. It is based on three operators: cannibalism, crossover, and mutation, whose main stage is Cannibalism. The advantage of this stage is that those designs that do not match the solutions close to the global optimal are eliminated, and the more effective solutions remain. To examine the proposed approach, five optimization examples, including three two-dimensional benchmark frames and two three-dimensional structures, have been used. The results show that the greedy sensitivity-based PSO technique can improve computational efficiency in solving discrete variable structural optimization problems. The hybridized BWO (BGP) with this technique was able to obtain very good results in terms of convergence speed and performance accuracy. Overall, compared to the performance of BWO, between 50 and 75% improvement in the total number of analyzes was achieved. In addition, a slight improvement in the weight of the evaluated structures was also reported. Compared to other hybrid algorithms, very competitive and promising results were obtained

Otimização estrutural de pórticos espaciais de aço via algoritmos de evolução diferencial

Spatial steel frames are structural systems widely applied in the most diverse branches of civil engineering. Common applications of this type of structure are found in residential and commercial buildings, industrial sheds, warehouses, airports, hospitals, cultural centers such as museums, sports stadiums, among others. In taller buildings, factors such as horizontal displacements due to wind loads, natural frequencies of vibration, and global stability become more relevant. With the advancement of engineering, the need for increasingly competitive and optimized projects emerged, arousing the search for computational methodologies capable of solving such problems. In the last decades, meta-heuristics have proven increasing efficiency and robustness in solving problems of this nature. This dissertation makes a study of structural optimization via differential evolution algorithms applied to spatial steel frames, having as an innovative point the addition of constraints related to the dynamic behavior and the global stability of the structure, in general neglected. Three sets of experiments are conducted, in which analyses of braced and unbraced structural systems, as well as studies for cardinality constraints and automatic member grouping, are taken into account in the steel frames optimization problems.Os pórticos espaciais de aço são sistemas estruturais vastamente aplicados nos mais diversos ramos da engenharia civil. Aplicações comuns desse tipo de estrutura são encontradas em prédios residenciais ou comerciais, galpões industriais, almoxarifados, aeroportos, hospitais, centros culturais como museus, estádios desportivos, entre outros. À medida que as construções vão se tornando cada vez mais altas, fatores como deslocamentos horizontais devido às cargas de vento, frequências naturais de vibração e estabilidade global da estrutura passam a ser mais relevantes em relação as restrições de resistência. Com o avanço da engenharia, veio a necessidade de projetos cada vez mais competitivos e otimizados, despertando a procura de metodologias computacionais capazes de resolver tais problemas. Nas últimas décadas as meta-heurísticas vieram mostrando eficiência e robustez crescentes na solução de problemas dessa natureza. Esta dissertação faz um estudo de otimização estrutural via algoritmos de evolução diferencial aplicado aos pórticos espaciais de aço, tendo como caráter inovador a adição de restrições relativas ao comportamento dinâmico e à estabilidade global da estrutura, em geral negligenciadas. Três conjuntos de experimentos são conduzidos, nos quais análises de sistemas estruturais contraventados e não-contraventados, bem como estudos para restrição de cardinalidade e agrupamento automático de membros são levados em consideração na otimização das estruturas de aço.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment