Iran University of Science & Technology 

About: Iran University of Science & Technology is an academic journal. The journal publishes majorly in the area(s): Optimization problem & Metaheuristic. Over the lifetime, 661 publications have been published receiving 2999 citations.

A novel meta-heuristic algorithm: tug of war optimization

Abstract: This paper presents a novel population-based meta-heuristic algorithm inspired by the game of tug of war. Utilizing a sport metaphor the algorithm, denoted as Tug of War Optimization (TWO), considers each candidate solution as a team participating in a series of rope pulling competitions. The teams exert pulling forces on each other based on the quality of the solutions they represent. The competing teams move to their new positions according to Newtonian laws of mechanics. Unlike many other meta-heuristic methods, the algorithm is formulated in such a way that considers the qualities of both of the interacting solutions. TWO is applicable to global optimization of discontinuous, multimodal, non-smooth, and non-convex functions. Viability of the proposed method is examined using some benchmark mathematical functions and engineering design problems. The numerical results indicate the efficiency of the proposed algorithm compared to some other methods available in literature.

Optimum placement and properties of tuned mass dampers using hybrid genetic algorithms

Abstract: Tuned mass dampers (TMDs) systems are one of the vibration controlled devices used to reduce the response of buildings subject to lateral loadings such as wind and earthquake loadings. Although TMDs system has received much attention from researchers due to their simplicity, the optimization of properties and placement of TMDs is a challenging task. Most research studies consider optimization of TMDs properties. However, the placement of TMDs in a building is also important. This paper considers optimum placement as well as properties of TMDs. Genetic algorithms (GAs) is used to optimize the location and properties of TMDs. Because the location of TMDs at a particular floor of a building is a discrete number, it is represented by binary coded genetic algorithm (BCGA), whereas the properties of TMDS are best suited to be represented by using real coded genetic algorithm (RCGA). The combination of these optimization tools represents a hybrid coded genetic algorithm (HCGA) that optimizes discrete and real values of design variables in one arrangement. It is shown that the optimization tool presented in this paper is stable and has the ability to explore an unknown domain of interest of the design variables, especially in the case of real coding parts. The simulation of the optimized TMDs subject to earthquake ground accelerations shows that the present approaches are comparable and/or outperform the available methods.

Optimum design of structures using an improved firefly algorithm

Abstract: Nature-inspired search algorithms have proved to be successful in solving real-world optimization problems. Firefly algorithm is a novel meta-heuristic algorithm which simulates the natural behavior of fireflies. In the present study, optimum design of truss structures with both sizing and geometry design variables is carried out using the firefly algorithm. Additionally, to improve the efficiency of the algorithm, modifications in the movement stage of artificial fireflies are proposed. In order to evaluate the performance of the proposed algorithm, optimum designs found are compared to the previously reported designs in the literature. Numerical results indicate the efficiency and robustness of the proposed approach.

Evaluation of concrete compressive strength using artificial neural network and multiple linear regression models

Abstract: In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and watercement ratio were considered as input variables. For each set of these input variables, the 28 days compressive strength of concrete were determined. A total number of 140 input-target pairs were gathered, divided into 70%, 15%, and 15% for training, validation, and testing steps in artificial neural network model, respectively, and divided into 85% and 15% for training and testing steps in multiple linear regression model, respectively. Comparing the testing steps of both of the models, it can be concluded that the artificial neural network model is more capable in predicting the compressive strength of concrete in compare to multiple linear regression model. In other words, multiple linear regression model is better to be used for preliminary mix design of concrete, and artificial neural network model is recommended in the mix design optimization and in the case of higher accuracy requirements.

Optimization of endurance time acceleration functions for seismic assessment of structures

Abstract: Numerical simulation of structural response is a challenging issue in earthquake engineering and there has been remarkable progress in this area in the last decade. Endurance Time (ET) method is a new response history based analysis procedure for seismic assessment and structural design in which structures are subjected to a gradually intensifying dynamic excitation and their seismic performance is evaluated based on their responses at different excitation levels. Generating appropriate artificial dynamic excitation is essential in this type of analysis. In this paper, an optimization procedure is presented for computation of the intensifying acceleration functions utilized in the ET method and the results of this procedure are discussed. A set of the ET acceleration functions (ETAFs) is considered which has been produced utilizing numerical optimization considering 2048 acceleration points as optimization variables by an unconstrained optimization procedure. The ET formulation is then modified from the continuous time condition into the discrete time state; thus the optimization problem is reformulated as a nonlinear least squares problem. In this way, a second set of the ETAFs is generated which better satisfies the proposed objective function. Subsequently, acceleration points are increased to 4096, for 40 seconds duration, and the third set of the ETAFs is produced using a multi level optimization procedure. Improvement of the ETAFs is demonstrated by analyzing several SDOF systems.