• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.855-868
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• 1996

In this paper, multiplhase only filter(MPOFs) are designed using simulated annealing algorithm. These filters have excellent recognition characteristics for similar patterns or rotated patterns and enhance optical efficiency as well as spatial-bandwidth product by deleting mirror image. As the result of computer simulation to certify recogntion characteristics of similar patterns, simulated annealing-MPOF(SA-MPOF) has superior discrimination and higher correlation peak values than cosine binary phase only filters(CBPOF) and simpulated annealing-BPOF (SA-BPOF). THe filter having training process for rotated patterns of arbitraty possible angle can overcome that phase only filter(POF) and CBPOF can't recognize rotated input patterns.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.3
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• pp.341-347
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• 2011

The redundancy allocation problem (RAP) is a famous NP.complete problem that has beenstudied in the system reliability area of ships and airplanes. Recently meta-heuristic techniques have been applied in this topic, for example, genetic algorithms, simulated annealing and tabu search. In particular, tabu search (TS) has emerged as an efficient algorithmic approach for the series-parallel RAP. However, the quality of solutions found by TS depends on the initial solution. As a robust and efficient methodology for the series-parallel RAP, the hybrid metaheuristic (TSA) that is a interactive procedure between the TS and SA (simulated annealing) is developed in this paper. In the proposed algorithm, SA is used to find the diversified promising solutions so that TS can re-intensify search for the solutions obtained by the SA. We test the proposed TSA by the existing problems and compare it with the SA and TS algorithm. Computational results show that the TSA algorithm finds the global optimal solutions for all cases and outperforms the existing TS and SA in cases of 42 and 56 subsystems.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.2
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• pp.93-100
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• 2008

In this paper, we address a novel simulated annealing(SA)-based test scheduling method for testing network-on-chip (NoC)-based systems-on-chip(SoCs), on the assumption that the test platform proposed in [1] is installed. The proposed method efficiently mixed the rectangle packing method with SA and improved the scheduling results by locally changing the test access mechanism(TAM) widths for cores and the testing orders. Experimental results using ITC'02 benchmark circuits show that the proposed algorithm can efficiently reduce the overall test time.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.236-243
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• 2005

This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.936-943
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• 2002

The Simulated Annealing (SA) algorithm has been successfully applied to various difficult combinatorial optimization problems. Since the performance of a SA algorithm, generally depends on values of the parameters, it is important to select the most appropriate parameter values. In this paper the SA algorithm is optimally designed for performance acceleration, by using the Taguchi method. Several test problems are solved via the SA algorithm optimally designed, and the solutions obtained are compared to solution results McMullen & Frazier(2000). The performance of the SA algorithm is evaluated in terms of solution quality and computation times. Computational results show that the proposed SA algorithm is effective and efficient in finding near-optimal solutions.

• Journal of Korean Society of Steel Construction
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• v.27 no.2
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• pp.131-142
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• 2015

In this paper, we present an optimization of truss structures subjected to stress, buckling, and natural frequency constraints. The main objective of the present study is to propose an efficient HA-SA algorithm for solving the truss optimization subject to multiple constraints. The procedure of hybrid HA-SA is a search method which a design values in harmony memory of harmony search are used as an initial value designs in simulated annealing search method. The efficient optimization of HA-SA is illustrated through several optimization examples. The examples of truss structures are used 10-Bar truss, 52-Bar truss (Dome), and 72-Bar truss for natural frequency constraints, and used 18-Bar truss and 47-Bar (Tower) truss for stress and buckling constraints. The optimum results are compared to those of different techniques. The numerical results are demonstrated the advantages of the HA-SA algorithm in truss optimization with multiple constraints.

• Korean Journal of Optics and Photonics
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• v.16 no.1
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• pp.43-49
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• 2005

In this paper, we propose a method for optimizing a computer-generated hologram(CGH) by combining the Tabu Search(TS) algorithm with the Simulated Annealing(SA) algorithm. By replacing an initial random pattern of the SA algorithm with an approximately ideal hologram pattern of the TS algorithm, we design a CGH which has high diffraction efficiency(DE). We compared the performance of the proposed algorithm with the SA algorithm using computer simulation and an optical experiment. As a result, we confirmed diffraction efficiency and uniformity to be enhanced in the proposed algorithm.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.04a
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• pp.102-109
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• 1999

Two phase simulated annealing algorithm is presented as a structural optimization technique and applied to minimum weight design of space trusses subjected to stress and displacement constraints under multiple loading conditions. Univariate searching algorithm is adopted for automatic selection of initial values of design variables for SA algorithm. The proper values of cooling factors and reasonable stopping criteria for optimum design of space truss structures are proposed to enhance the performance of optimization process. Optimum weights and design solutions are presented for two well-blown example structures and compared with those reported in the literature.

• Journal of Korea Multimedia Society
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• v.9 no.1
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• pp.119-126
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• 2006

The task scheduling in multiprocessor system is one of the key elements in the effective utilization of multiprocessor systems. The optimal assignment of tasks to multiprocessor is, in almost practical cases, an NP-hard problem. Consequently algorithms based on various modern heuristics have been proposed for practical reason. This paper proposes a new task scheduling algorithm using Genetic Algorithm which combines simulated annealing (GA+SA) in multiprocessor environment. In solution algorithms, the Genetic Algorithm (GA) and the simulated annealing (SA) are cooperatively used. In this method, the convergence of GA is improved by introducing the probability of SA as the criterion for acceptance of new trial solution. The objective of proposed scheduling algorithm is to minimize makespan. The effectiveness of the proposed algorithm is shown through simulation studies. In simulation studies, the result of proposed algorithm is better than that of any other algorithms.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.132-144
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• 2020

Cloud computing is an emerging technology based on the concept of enabling data access from anywhere, at any time, from any platform. The exponential growth of cloud users has resulted in the emergence of multiple issues, such as the workload imbalance between the virtual machines (VMs) of data centers in a cloud environment greatly impacting its overall performance. Our axis of research is the load balancing of a data center's VMs. It aims at reducing the degree of a load's imbalance between those VMs so that a better resource utilization will be provided, thus ensuring a greater quality of service. Our article focuses on two phases to balance the workload between the VMs. The first step will be the determination of the threshold of each VM before it can be considered overloaded. The second step will be a task allocation to the VMs by relying on an improved and faster version of the meta-heuristic "simulated annealing (SA)". We mainly focused on the acceptance probability of the SA, as, by modifying the content of the acceptance probability, we could ensure that the SA was able to offer a smart task distribution between the VMs in fewer loops than a classical usage of the SA.