• Advances in Computational Design
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• v.1 no.1
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• pp.79-86
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• 2016

In this study, the three different meta-heuristics namely the Grey Wolf Optimizer (GWO), Stochastic Fractal Search (SFS), and Adaptive Differential Evolution with Optional External Archive (JADE) algorithms are examined. This study considers optimization of the planer frame to minimize its weight subjected to the strength and displacement constraints as per the American Institute of Steel and Construction - Load and Resistance Factor Design (AISC-LRFD). The GWO algorithm is associated with grey wolves' activities in the social hierarchy. The SFS algorithm works on the natural phenomenon of growth. JADE on the other hand is a powerful self-adaptive version of a differential evolution algorithm. A one-bay ten-story planar steel frame problem is examined in the present work to investigate the design ability of the proposed algorithms. The frame design is produced by optimizing the W-shaped cross sections of beam and column members as per AISC-LRFD standard steel sections. The results of the algorithms are compared. In addition, these results are also mapped with other state-of-art algorithms.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.131-136
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• 2018

Harmony search (HS) is a relatively recently developed meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments' pitches searching for a perfect state of harmony. In the conventional HS algorithm, it is necessary to determine the maximum number of iterations with some algorithm parameters. However, there is no criterion for determining the number of iterations, which is a very difficult problem. To solve this problem, a new method is proposed to perform the algorithm without setting the maximum number of iterations in this paper. The new method allows the algorithm to be performed until the desired tuning is achieved. To do this, a new variable bandwidth is introduced. In addition, the types and probability of harmonies composed of variables is analyzed to help to decide the value of HMCR. The performance of the proposed method is investigated and compared with classical HS. The experiments conducted show that the new method generally outperformed conventional HS when applied to seven benchmark problems.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.797-802
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• 2000

Flow shop scheduling problem involves processing several jobs on common facilities where a setup time Is incurred whenever there is a switch of jobs. Practical aspect of scheduling focuses on finding a near-optimum solution within a feasible time rather than striving for a global optimum. In this paper, a hybrid meta-heuristic method called tabu-genetic algorithm(TGA) is suggested, which combines the genetic algorithm(GA) with tabu list. The experiment shows that the proposed TGA can reach the optimum solution with higher probability than GA or SA(Simulated Annealing) in less time than TS(Tabu Search). It also shows that consideration of setup time becomes more important as the ratio of setup time to processing time increases.

• Journal of the Korean Society for Railway
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• v.9 no.1 s.32
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• pp.76-80
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• 2006

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.238-240
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• 2005

The Optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model have been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme are utilized, respectively. As a result, two meta-models for trip time and energy consumption were constructed. The optimization to search an economical running pattern was achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.594-600
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• 2006

The optimization has been performed to search an economical running pattern in the view point of trip time and energy consumption. Fuzzy control model has been applied to build the meta-model. To identify the structure and its parameters of a fuzzy model, fuzzy c-means clustering method and differential evolutionary scheme ate utilized, respectively. As a result, two meta-models for trip time and energy consumption are constructed. The optimization to search an economical running pattern is achieved by differential evolutionary scheme. The result shows that the proposed methodology is very efficient and conveniently applicable to the operation of railway system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.236-244
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• 2012

The minimum energy broadcast problem is for all deployed nodes to minimize a total transmission energy for performing a broadcast operation in wireless networks. In this paper, we propose a Tabu search algorithm to solve efficiently the minimum energy broadcast problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the total transmission energy of nodes and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the minimum energy broadcast problem in wireless sensor networks.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.237-242
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• 2003

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.42-48
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• 2004

This paper concerns on a multiprocessor task scheduling problem with precedence relation, in which each task requires several processors simultaneously. Meta-heuristic generally finds a good solution if it starts from a good solution. In this paper, a tabu search is presented to find a schedule of minimal time to complete all tasks. A modified tabu search is also presented which uses a new initial solution based on the best solution during the previous run as the new starting solution for the next iteration. Numerical results show that a tabu search and a modified tabu search yield a better performance than the previous studies.