• Journal of Computational Design and Engineering
• /
• v.3 no.1
• /
• pp.24-36
• /
• 2016

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

• Journal of Fisheries and Marine Sciences Education
• /
• v.29 no.2
• /
• pp.544-551
• /
• 2017

Metaheuristic algorithms are efficient techniques for a class of mathematical optimization problems without having to deeply adapt to the inherent nature of each problem. They are very useful for structural design optimization in which the cost of gradient computation can be very expensive. Among them, the characteristics of simulated annealing and genetic algorithms are briefly discussed. In Metropolis genetic algorithm, favorable features of Metropolis criterion in simulated annealing are incorporated in the reproduction operations of simple genetic algorithm. Numerical examples of structural design optimization are presented. The example structures are truss, breakwater and steel box girder bridge. From the theoretical evaluation and numerical experience, performance and applicability of metaheuristic algorithms for structural design optimization are discussed.

• Journal of Korean Institute of Industrial Engineers
• /
• v.38 no.4
• /
• pp.262-275
• /
• 2012

This paper develops a multistage scheduling algorithm for the module operation of the LCD(Liquid Crystal Display) production systems and tests the efficiency of the proposed algorithm. The module operation is a multistage form composed of multiple sub operations of processes, and each stage is consists of multiple lines with the same kinds of machines. This paper presents a mathematical modeling reflecting the constraints of the LCD module operation and develops a multistage scheduling algorithm based on tabu search metaheuristic approach. For this purpose, an production order is assigned to a line of the sub operations and a sequence of the assigned order is rearranged to draw an efficient schedule. Simulation experiments test performance measures and show the efficiency of the proposed algorithm.

• Structural Engineering and Mechanics
• /
• v.75 no.2
• /
• pp.175-191
• /
• 2020

In this study, multi-story structures are actively controlled using metaheuristic algorithms. The soil conditions such as dense, normal and soft soil are considered under near-fault ground motions consisting of two types of impulsive motions called directivity effect (fault normal component) and the flint step (fault parallel component). In the active tendon-controlled structure, Proportional-Integral-Derivative (PID) type controller optimized by the proposed algorithms was used to achieve a control signal and to produce a corresponding control force. As the novelty of the study, the parameters of PID controller were determined by different metaheuristic algorithms to find the best one for seismic structures. These algorithms are flower pollination algorithm (FPA), teaching learning based optimization (TLBO) and Jaya Algorithm (JA). Furthermore, since the influence of time delay on the structural responses is an important issue for active control systems, it should be considered in the optimization process and time domain analyses. The proposed method was applied for a 15-story structural model and the feasible results were found by limiting the maximum control force for the near-fault records defined in FEMA P-695. Finally, it was determined that the active control using metaheuristic algorithms optimally reduced the structural responses and can be applied for the buildings with the soil-structure interaction (SSI).

• The KIPS Transactions:PartB
• /
• v.16B no.5
• /
• pp.411-426
• /
• 2009

Most of the metaheuristics are made by imitating the action of the animals. In this paper, we proposed Mine Algorithm. The Mine Algorithm is a metaheuristic that imitates the action of the human being. Speaking of search, the field in which the know-how and the heuristics of the human being are melted best is the mining industry. In the Mine Algorithm we formalize the action pattern of the human being by focusing the mine business. The Mine Algorithm uses various searching techniques fluently and shows equally good performance for broad problems. That is, it has good generality. We show the improved generality of the Mine Algorithm by the comparing experiments with the conventional metaheuristics.

• Smart Structures and Systems
• /
• v.26 no.3
• /
• pp.263-275
• /
• 2020

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

• Steel and Composite Structures
• /
• v.33 no.5
• /
• pp.747-753
• /
• 2019

Metaheuristic algorithm is used to solve the weight minimization problem of truss structures considering shape, and sizing design variables. The cross-sectional areas of the line element in trusses are the design variables for size optimization and the changeable joint coordinates are the shape optimization used in this study. The design of plane and spatial truss structures are optimized by metaheuristic technique named Teaching-Learning-Based Optimization (TLBO). Finite element analyses of structures and optimization process are carried out by the computer program visually developed by the authors coded in MATLAB. The four benchmark problems (trusses 2D ten-bar, 3D thirty-seven-bar, 3D seventy-two-bar and 2D two-hundred-bar) taken from literature are optimized and the optimal solution compared the results given by previous studies.

• Journal of Digital Convergence
• /
• v.12 no.5
• /
• pp.125-130
• /
• 2014

The competitiveness in today's global market forces many companies to develop families of products to provide enough variety for the marketplace. The challenge when designing a product family is in resolving the tradeoff between product commonality and distinctiveness. Simultaneously it is necessary to consider environmental performance to design a product family as well as to shorten lead-times, improve quality and reduce costs. This paper proposes a metaheuristic algorithm based redesign methodology for green product family considering environmental performance. The proposed method uses a genetic algorithm as metaheuristic algorithm and green product family index (GPFI) to support green product family design. In addition, it provides the redesign methodology such as product family level and component level. A case study used table lamps as an product family's example shows the verification and effectiveness of the proposed method.

• Structural Engineering and Mechanics
• /
• v.45 no.5
• /
• pp.643-654
• /
• 2013

Optimum cost design of columns subjected to axial force and uniaxial bending moment is presented in this paper. In the formulation of the optimum design problem, the height and width of the column, diameter and number of reinforcement bars are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the column consisting the cost of concrete, steel, and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The Artificial Bee Colony Algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.

• Computers and Concrete
• /
• v.10 no.3
• /
• pp.295-306
• /
• 2012

Optimum cost design of a simply supported reinforced concrete beam is presented in this paper. In the formulation of the optimum design problem, the height and width of the beam, and reinforcement steel area are treated as design variables. The design constraints are implemented according to ACI 318-08 and studies in the literature. The objective function is taken as the cost of unit length of the beam consisting the cost of concrete, steel and shuttering. The solution of the design problem is obtained using the artificial bee colony algorithm which is one of the recent additions to metaheuristic techniques. The artificial bee colony algorithm is imitated the foraging behaviors of bee swarms. In application of this algorithm to the constraint problem, Deb's constraint handling method is used. Obtained results showed that the optimum value of numerical example is nearly same with the existing values in the literature.