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Journal ArticleDOI: 10.1080/0305215X.2020.1726341

Size and layout optimization of truss structures with dynamic constraints using the interactive fuzzy search algorithm

04 Mar 2021-Engineering Optimization (Taylor & Francis)-Vol. 53, Iss: 3, pp 369-391
Abstract: Optimizing truss structures considering natural frequency constraints can fundamentally enhance their dynamic behaviour under transient loadings. In this regard, the current investigation assesses ...

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Topics: Fuzzy logic (58%), Truss (55%), Transient (oscillation) (51%) ... show more

6 results found

Open accessDOI: 10.18720/CUBS.92.4
01 Jan 2020-
Abstract: The object of research is the statically determinate cantilever truss. The trass consists of rectangular panels with downward diagonal beams. The truss has two supports, one of which is fixed hinged, and another one is roller support. Masses are located in the nodes of top and bottom chords. Forces in the bars and reactions at supports are determined using the method of joint isolation. The vertical displacement of nodes is derived from the Maxwell-Mohr method with the premise of linear elasticity. Dependence of vertical displacement, Dunkerley’s and Rayleigh’s estimations of primary truss frequency on the number of panels is deduced from the inductive analysis of the set of particular trusses with an increasing number of panels. Recurrence equations that meet particular coefficients are derived using special functions of the computer algebra system Maple. Obtained solutions are polynomial, with the number of panels as variables. Rayleigh’s quotient is calculated with the assumption that the first mode of vibration is equal to truss deflection under the uniformly distributed load. Graphs of the dependencies of obtained estimations on nodes masses, the number of panels, stiffness, and size of the truss are plotted.

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Topics: Plane (geometry) (57%), Truss (56%), Maple (51%)

6 Citations

Journal ArticleDOI: 10.1016/J.ADVENGSOFT.2021.102994
Ali Mortazavi1Institutions (1)
Abstract: Metaheuristic algorithms are general optimization techniques that demonstrate remarkable performance in solving different classes of optimization problems. However, equipping their stochastic search mechanisms with auxiliary logical strategies can still increase their search capability. Based on this fact, in the current study, the search performance of the Interactive Search Algorithm (ISA), as a metaheuristic search method, is improved by adding a new Bayesian regulator strategy to adjust its search behavior. In this regard, the search patterns of the ISA method are unified and classified according to the memory and learning concepts. Subsequently, during the optimization process, the developed Bayesian module dynamically regulates the ratio of the exploration and exploitation search behaviors by tuning the effect of memory concept. The recent technique is named Bayesian Interactive Search Algorithm (BISA), and its search performance tested on a suite of unconstraint mathematical functions and constrained engineering problems. Acquired outcomes indicate that the proposed BISA considerably speeds up the convergence rate, and improves the stability of the process as well as the accuracy of the solutions, for both engineering and mathematical problems.

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Topics: Metaheuristic (67%), Swarm intelligence (56%), Optimization problem (56%) ... show more

3 Citations

Journal ArticleDOI: 10.1016/J.KNOSYS.2021.107291
Ali Mortazavi1, Mahsa Moloodpoor1Institutions (1)
Abstract: Butterfly Optimization Algorithm (BOA) is a recently developed metaheuristic search algorithm that mimics the food-search process of the butterflies in nature. The studies reveal that BOA shows significant performance on the non-constrained and unbiased mathematical functions, but in the cases of shifted and rotated functions and/or constrained optimization problems, its search capacity is considerably restricted. To address this shortcoming, the present study deals with developing a new fuzzy decision-making strategy and introducing a new auxiliary concept called “virtual butterfly” to enhance the search capability of the standard BOA. The developed fuzzy strategy permanently monitors the optimization process and tries to adjust each butterfly’s search behavior based on the governing conditions of the current problem. Also, the virtual butterfly concept involving the information of the whole colony tries to provide alternative promising search directions for the other butterflies. The new reinforced method is named Fuzzy Butterfly Optimization Algorithm (FBOA). To evaluate the search performance of the proposed FBOA, it is tested on solving a suit of constrained and non-constrained optimization problems and the achieved outcomes are compared with those given by some other well-established techniques including its parent method (i.e. BOA). The results show that the implemented improvements significantly increase the search capability of the regular BOA, especially in the case of constrained engineering problems.

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Topics: Metaheuristic (61%), Search algorithm (57%), Optimization problem (55%) ... show more

2 Citations

Journal ArticleDOI: 10.1016/J.ISTRUC.2020.10.016
01 Dec 2020-Structures
Abstract: This paper presents both discrete and continuous sizing, shape, and layout structural optimization problems concerning the weight minimization of dome structures and considering displacements, stresses, natural frequencies of vibration, and global stability as constraints. The layout optimization searches for the best structural configuration through the maintenance of the bars’ grouping by introducing a new design variable. This variable refers to the number of standard modules used to generate the structural configuration of optimized domes. It can be attractive to use a reduced number of distinct cross-sectional areas, minimizing the costs of fabrication, transportation, storing, checking, and welding, providing labor saving measures. To obtain an automatic member grouping of the bars of the trusses analyzed in this paper, a specific encoding using cardinality constraints is considered. As result, trade-off curves are provided, showing the optimized weights in comparison with the number of distinct cross-sectional areas used in the solutions. Differential Evolution is the search algorithm adopted in this paper. The structural optimization problems showed better solutions when compared with those presented in the literature.

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2 Citations

Journal ArticleDOI: 10.1016/J.ESWA.2021.115954
Abstract: The current study deals with introducing a new probabilistic, self-adaptive, and gradient-free search algorithm. In the proposed method new Bayesian and Fuzzy auxiliary mechanisms are defined and simultaneously employed to extremely adjust the trade-off between exploration and exploitation search behaviors of the swarm-based technique so-called Interactive Search Algorithm (ISA). In this regard, a nine-rule fuzzy decision-making strategy and a hierarchical forecasting Bayesian formulation are developed. The integrated fuzzy and Bayesian mechanisms permanently monitor the search process, and try to dynamically tune the search behavior of each agent based on the governing conditions of the current problem and cause the proposed method to work as a self-adaptive search algorithm. This new search technique is named Interactive Fuzzy Bayesian Search Algorithm (IFBSA) and its performance is tested on a suit of unconstrained mathematical functions and constrained structural and mechanical optimization problems with different properties. Acquired outcomes demonstrate that the proposed IFBSA, thanks to its dual supplementary module, provides promising and superior results in the terms of accuracy, stability, and convergence rate.

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Topics: Search algorithm (64%), Fuzzy logic (57%), Bayesian search theory (57%) ... show more


45 results found

Journal ArticleDOI: 10.1016/J.CAD.2010.12.015
Abstract: A new efficient optimization method, called 'Teaching-Learning-Based Optimization (TLBO)', is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the 'Teacher Phase' and the second part consists of the 'Learner Phase'. 'Teacher Phase' means learning from the teacher and 'Learner Phase' means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.

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2,400 Citations

Journal ArticleDOI: 10.1016/J.ASOC.2011.01.037
01 Jun 2011-
Abstract: Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by intelligent collective behavior of some animals. The most important advantages of the PSO are that PSO is easy to implement and there are few parameters to adjust. The inertia weight (w) is one of PSO's parameters originally proposed by Shi and Eberhart to bring about a balance between the exploration and exploitation characteristics of PSO. Since the introduction of this parameter, there have been a number of proposals of different strategies for determining the value of inertia weight during a course of run. This paper presents the first comprehensive review of the various inertia weight strategies reported in the related literature. These approaches are classified and discussed in three main groups: constant, time-varying and adaptive inertia weights. A new adaptive inertia weight approach is also proposed which uses the success rate of the swarm as its feedback parameter to ascertain the particles' situation in the search space. The empirical studies on fifteen static test problems, a dynamic function and a real world engineering problem show that the proposed particle swarm optimization model is quite effective in adapting the value of w in the dynamic and static environments.

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Topics: Multi-swarm optimization (62%), Particle swarm optimization (60%), Swarm behaviour (55%) ... show more

567 Citations

Journal ArticleDOI: 10.1007/S00500-018-3102-4
Sankalap Arora1, Satvir Singh1Institutions (1)
01 Feb 2019-
Abstract: Real-world problems are complex as they are multidimensional and multimodal in nature that encourages computer scientists to develop better and efficient problem-solving methods. Nature-inspired metaheuristics have shown better performances than that of traditional approaches. Till date, researchers have presented and experimented with various nature-inspired metaheuristic algorithms to handle various search problems. This paper introduces a new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems. The framework is mainly based on the foraging strategy of butterflies, which utilize their sense of smell to determine the location of nectar or mating partner. In this paper, the proposed algorithm is tested and validated on a set of 30 benchmark test functions and its performance is compared with other metaheuristic algorithms. BOA is also employed to solve three classical engineering problems (spring design, welded beam design, and gear train design). Results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.

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353 Citations

Journal ArticleDOI: 10.1016/J.COMPSTRUC.2009.04.011
Ali Kaveh1, Siamak Talatahari2Institutions (2)
Abstract: A Hybrid Big Bang-Big Crunch (HBB-BC) optimization algorithm is employed for optimal design of truss structures. HBB-BC is compared to Big Bang-Big Crunch (BB-BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB-BC method compared to other heuristic algorithms.

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Topics: Metaheuristic (66%), Meta-optimization (66%), Multi-swarm optimization (65%) ... show more

351 Citations