Metaheuristics in structural optimization and discussions on harmony search algorithm (original) (raw)
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A Comparative Study of Three Metaheuristics for Optimum Design of Engineering Structures
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A new structural optimization method based on the harmony search algorithm
Computers & Structures, 2004
Most structural optimization methods are based on mathematical algorithms that require substantial gradient information. The selection of the starting values is also important to ensure that the algorithm converges to the global optimum. This paper describes a new structural optimization method based on the harmony search (HS) meta-heuristic algorithm, which was conceptualized using the musical process of searching for a perfect state of harmony. The HS algorithm does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. Various truss examples with fixed geometries are presented to demonstrate the effectiveness and robustness of the new method. The results indicate that the new technique is a powerful search and optimization method for solving structural engineering problems compared to conventional mathematical methods or genetic algorithm-based approaches.
Efficiency and Robustness of Three Metaheuristics in the Framework of Structural Optimization
IFIP Advances in Information and Communication Technology, 2010
Due to the technological advances in computer hardware and software tools, structural optimization has been gaining continuously increasing interest over the last two decades. The purpose of the present work is to quantitatively compare three metaheuristic optimization algorithms, namely the Differential Evolution, Harmony Search and Particle Swarm Optimization methods, in the framework of structural optimization. The comparison of the optimizers is performed with reference to their efficiency (overall computing demands) and robustness (capability to detect near-optimal solutions). The optimum design of a real-world overhead traveling crane is used as the test bed application for conducting optimization test runs.
Optimal construction design using the Harmony Search Algorithm as an optimization tool
The determination of the optimal values of the numerous parameters involved in the design of complex construction elements has always been a challenge for engineers. The use of optimization approaches and techniques has gradually replaced the practice of empirical determination of crucial design parameters. The quest for more robust but at the same time user-friendly optimization techniques is nowadays more intense than ever. This need to develop and apply new optimization techniques in order to achieve more efficient and effective solutions in complex management and design problems, has led to the introduction of a series of interesting new methods. Harmony Search Algorithm (HSA) is a music-based meta-heuristic algorithm and its applications cover a wide range of scientific fields. In this paper, the main features of this method are presented along with a specific construction design application. This application is performed using specially-designed computer software programmed in MATLAB environment. The created software includes a user-friendly GUI (Graphical User Interface) that allows the user to easily interact with the optimization tool.
EFFICIENCY OF IMPROVED HARMONY SEARCH ALGORITHM FOR SOLVING ENGINEERING OPTIMIZATION PROBLEMS
Many optimization techniques have been proposed since the inception of engineering optimization in 1960s. Traditional mathematical modeling-based approaches are incompetent to solve the engineering optimization problems, as these problems have complex system that involves large number of design variables as well as equality or inequality constraints. In order to overcome the various difficulties encountered in obtaining the solution of these problems, new techniques called metaheuristic algorithms are suggested. These techniques are numerical optimization algorithms that are based on a natural phenomenon. In this study, a state-of-art improved harmony search method with a new adaptive error strategy is proposed to handle the design constraints. Number of numerical examples is presented to demonstrate the efficiency of the proposed algorithm in solving engineering optimization problems.
Optimization in Structural Analysis and Design
2009
Two main tasks of a structural engineer, as for many other branches of engineering, are analysis and design. Among these two, the latter needs more knowledge, skill and experience. It even comprises completely the first one, that is, a designer must already have the capacity of analysis.
Harmony search algorithms in structural engineering
Computational Optimization and Applications in Engineering and Industry, 2011
Harmony search method is widely applied in structural design optimization since its emergence. These applications have shown that harmony search algorithm is robust, effective and reliable optimization method. Within recent years several enhancements are suggested to improve the performance of the algorithm. Among these Mahdavi has presented two versions of harmony search methods. He named these as improved harmony search method and global best harmony search method. Saka and Hasancebi (2009) ...
Evolution Strategies-Based Metaheuristics in Structural Design Optimization
Metaheuristic Applications in Structures and Infrastructures, 2013
During the last three decades, many numerical methods have been developed to meet the demands of structural design optimization. These methods can be classified in two categories, the deterministic methods and the probabilistic methods. Mathematical programming methods are the most popular methods of the first category and in particular the gradient-based optimizers. These methods make use of local curvature information, derived from linearization of the objective and constraint functions by using their derivatives with respect to the design variables at points obtained in the process of optimization, to construct an approximate model of the initial problem. Heuristic and metaheuristic algorithms are nature-inspired or bioinspired as they have been developed based on the successful evolutionary behavior of natural systems by learning from nature. These methods belong to the probabilistic category of methods. Modern metaheuristic algorithms for engineering optimization include genetic algorithms (GAs) , simulated annealing (SA) (Kirkpatrick et al., 1983), particle swarm optimization (PSO) (Kennedy and Eberhart, 1995), ant colony optimization (ACO) algorithm (Dorigo and Stützle, 2004), artificial bee colony (ABC) algorithm (Bozorg Haddad et al., 2005), harmony search (HS) algorithm (Geem et al., 2001), cuckoo search (CS) algorithm (Yang and Deb, 2010), firefly algorithm (FA) (Yang, 2008), bat algorithm (BA) (Yang and Gandomi, 2012), krill herd (KH) algorithm (Gandomi and Alavi, 2012), and many others. Evolutionary algorithms (EAs) are the most widely used class of metaheuristic algorithms and include evolutionary programming (EP) (Fogel,
2021
This paper presents several strategies to tune the parameters of metaheuristic methods for (discrete) design optimization of reinforced concrete (RC) structures. A novel utility metric is proposed, based on the area under the average performance curve. The process of modelling, analysis and design of realistic RC structures leads to objective functions for which the evaluation is computationally very expensive. To avoid costly simulations, two types of surrogate models are used. The first one consists of the creation of a database containing all possible solutions. The second one uses benchmark functions to create a discrete sub-space of them, simulating the main features of realistic problems. Parameter tuning of four metaheuristics is performed based on two strategies. The main difference between them is the parameter control established to perform partial assessments. The simplest strategy is suitable to tune good “generalist” methods, i.e., methods with good performance regardle...