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Showing 7 results for Big Bang-Big Crunch Algorithm

M.h. Afshar, I. Motaei,
Volume 1, Issue 2 (6-2011)
Abstract

A constrained version of the Big Bang-Big Crunch algorithm for the efficient solution of the optimal reservoir operation problems is proposed in this paper. Big Bang-Big Crunch (BB-BC) algorithm is a new meta-heuristic population-based algorithm that relies on one of the theories of the evolution of universe namely, the Big Bang and Big Crunch theory. An improved formulation of the algorithm named Constrained Big Bang-Big Crunch (CBB-BC) is proposed here and used to solve the problems of reservoir operation. In the CBB-BC algorithm, all the problems constraints are explicitly satisfied during the solution construction leading to an algorithm exploring only the feasible region of the original search space. The proposed algorithm is used to optimally solve the water supply and hydro-power operation of “Dez” reservoir in Iran over three different operation periods and the results are presented and compared with those obtained by the basic algorithm referred to here as Unconstrained Big Bang–Big Crunch (UBB–BC) algorithm and other optimization algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) and those obtained by Non-Linear Programming (NLP) technique. The results demonstrate the efficiency and robustness of the proposed method to solve reservoir operation problems compared to alternative algorithms.
S. Kazemzadeh Azad, O. Hasançebi, O. K. Erol,
Volume 1, Issue 3 (9-2011)
Abstract

Engineering optimization needs easy-to-use and efficient optimization tools that can be employed for practical purposes. In this context, stochastic search techniques have good reputation and wide acceptability as being powerful tools for solving complex engineering optimization problems. However, increased complexity of some metaheuristic algorithms sometimes makes it difficult for engineers to utilize such techniques in their applications. Big- Bang Big-Crunch (BB-BC) algorithm is a simple metaheuristic optimization method emerged from the Big Bang and Big Crunch theories of the universe evolution. The present study is an attempt to evaluate the efficiency of this algorithm in solving engineering optimization problems. The performance of the algorithm is investigated through various benchmark examples that have different features. The obtained results reveal the efficiency and robustness of the BB-BC algorithm in finding promising solutions for engineering optimization problems.
S. Kazemzadeh Azad, O. Hasançebi , S. Kazemzadeh Azad,
Volume 4, Issue 2 (6-2014)
Abstract

Computational cost of metaheuristic based optimum design algorithms grows excessively with structure size. This results in computational inefficiency of modern metaheuristic algorithms in tackling optimum design problems of large scale structural systems. This paper attempts to provide a computationally efficient optimization tool for optimum design of large scale steel frame structures to AISC-LRFD specifications. To this end an upper bound strategy (UBS), which is a recently proposed strategy for reducing the total number of structural analyses in metaheuristic optimization algorithms, is used in conjunction with an exponential variant of the well-known big bang-big crunch optimization algorithm. The performance of the UBS integrated algorithm is investigated in the optimum design of two large-scale steel frame structures with 3860 and 11540 structural members. The obtained numerical results clearly reveal the usefulness of the employed technique in practical optimum design of large-scale structural systems even using regular computers.
S. Talatahari, M. T. Aalami, R. Parsiavash,
Volume 6, Issue 2 (6-2016)
Abstract

This paper presents an efficient optimization procedure to find the optimal shapes of double curvature  arch  dams  considering  fluid–structure  interaction  subject  to  earthquake  loading. The optimization is carried out using a combination of the magnetic charged system search, big bang-big crunch algorithm and artificial neural network methods. Performing the finite element  analysis  during  the  optimization  process  is  time  consuming.  Back  propagation neural  network  is  utilized  to  reduce  the  computational  burden.  A  real-world  arch  dam  is considered as a numerical example to demonstrate the efficiency of the proposed method. The numerical results reveal the computational advantages of the new method for optimal 
design of arch dams.


S. Kazemzadeh Azad, S. Kazemzadeh Azad, O. Hasançebi,
Volume 6, Issue 3 (9-2016)
Abstract

The big bang-big crunch (BB-BC) algorithm is a popular metaheuristic optimization technique proposed based on one of the theories for the evolution of the universe. The algorithm utilizes a two-phase search mechanism: big-bang phase and big-crunch phase. In the big-bang phase the concept of energy dissipation is considered to produce disorder and randomness in the candidate population while in the big-crunch phase the randomly created solutions are shrunk into a single point in the design space. In recent years, numerous studies have been conducted on application of the BB-BC algorithm in solving structural design optimization instances. The objective of this review study is to identify and summarize the latest promising applications of the BB-BC algorithm in optimal structural design. Different variants of the algorithm as well as attempts to reduce the total computational effort of the technique in structural optimization problems are covered and discussed. Furthermore, an empirical comparison is performed between the runtimes of three different variants of the algorithm. It is worth mentioning that the scope of this review is limited to the main applications of the BB-BC algorithm and does not cover the entire literature.


A. Kaveh, K. Biabani Hamedani,
Volume 10, Issue 1 (1-2020)
Abstract

The minimum crossing number problem is among the oldest and most fundamental problems arising in the area of automatic graph drawing. In this paper, eight population-based meta-heuristic algorithms are utilized to tackle the minimum crossing number problem for two special types of graphs, namely complete graphs and complete bipartite graphs. A 2-page book drawing representation is employed for embedding graphs in the plane. The algorithms consist of Artificial Bee Colony algorithm, Big Bang-Big Crunch algorithm, Teaching-Learning-Based Optimization algorithm, Cuckoo Search algorithm, Charged System Search algorithm, Tug of War Optimization algorithm, Water Evaporation Optimization algorithm, and Vibrating Particles System algorithm. The performance of the utilized algorithms is investigated through various examples including six complete graphs and eight complete bipartite graphs. Convergence histories of the algorithms are provided to better understanding of their performance. In addition, optimum results at different stages of the optimization process are extracted to enable to compare the meta-heuristics algorithms.
B. Eftekhar, O. Rezaifar, A. Kheyroddin ,
Volume 10, Issue 2 (4-2020)
Abstract

Among the different lateral force resisting systems, shear walls are of appropriate stiffness and hence are extensively employed in the design of high-rise structures. The architectural concerns regarding the safety of these structures have further widened the application of coupled shear walls. The present study investigated the optimal dimensional design of coupled shear walls based on the improved Big Bang-Big Crunch algorithm. This optimization method achieves unique solutions in a short period according to the defined objective function, design variables, and constraints. Moreover, the results of the present study indicated that the dimensions of the coupling beam in the shear wall significantly affect the wall behavior by maximizing its efficiency which implies on its practical application by considering the wall in the flexural model.

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