• Journal of the Korea Safety Management & Science
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• v.13 no.3
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• pp.107-114
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• 2011

Traveling salesman problem is to minimize the total cost for a traveling salesman who wants to make a tour given finite number of cities along with the cost of travel between each pair them, visiting each cities exactly once before returning home. Traveling salesman problem is known to be NP-hard, and it needs a lot of computing time to get the optimal solution, so that heuristics are more frequently developed than optimal algorithms. This study suggests a hybrid parallel genetic algorithm(HPGA) for traveling salesman problem The suggested algorithm combines parallel genetic algorithm, nearest neighbor search, and 2-opt. The suggested algorithm has been tested on 7 problems in TSPLIB and compared the results of existing methods(heuristics, meta-heuristics, hybrid, and parallel). Experimental results shows that HPGA could obtain good solution in total travel distance minimization.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.360-368
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• 1999

We consider the problem of scheduling n jobs with sequence-dependent processing times on a set of parallel-identical machines. The processing time of each job consists of a pure processing time and a sequence-dependent setup time. The objective is to maximize the total remaining machine available time which can be used for other tasks. For the problem, a hybrid genetic algorithm is proposed. The algorithm combines a genetic algorithm for global search and a heuristic for local optimization to improve the speed of evolution convergence. The genetic operators are developed such that parallel machines can be handled in an efficient and effective way. For local optimization, the adjacent pairwise interchange method is used. The proposed hybrid genetic algorithm is compared with two heuristics, the nearest setup time method and the maximum penalty method. Computational results for a series of randomly generated problems demonstrate that the proposed algorithm outperforms the two heuristics.

• IE interfaces
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• v.23 no.2
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• pp.147-155
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• 2010

Reliability allocation is defined as a problem of determination of the reliability for subsystems and components to achieve target system reliability. The determination of both optimal component reliability and the number of component redundancy allowing mixed components to maximize the system reliability under resource constraints is called reliability-redundancy allocation problem(RAP). The main objective of this study is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for reliability-redundancy allocation problem that decides both optimal component reliability and the number of component redundancy to maximize the system reliability under cost and weight constraints. The global optimal solutions of each example are obtained by using CPLEX 11.1. The component structure, reliability, cost, and weight were computed by using HPGA and compared the results of existing metaheuristic such as Genetic Algoritm(GA), Tabu Search(TS), Ant Colony Optimization(ACO), Immune Algorithm(IA) and also evaluated performance of HPGA. The result of suggested algorithm gives the same or better solutions when compared with existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improve solution through swap, 2-opt, and interchange processes. In order to calculate the improvement of reliability for existing studies and suggested algorithm, a maximum possible improvement(MPI) was applied in this study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.48-55
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• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.4
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• pp.566-578
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• 2017

For optimizing the operation plan when strike packages attack multiple targets, this article suggests a new mathematical model and a parallel hybrid genetic algorithm (PHGA) as a solution methodology. In the model, a package can assault multiple targets on a sortie and permitted the use of mixed munitions for a target. Furthermore, because the survival probability of a package depends on a flight route, it is formulated as a mixed integer programming which is synthesized the models for vehicle routing and weapon-target assignment. The hybrid strategy of the solution method (PHGA) is also implemented by the separation of functions of a GA and an exact solution method using ILOG CPLEX. The GA searches the flight routes of packages, and CPLEX assigns the munitions of a package to the targets on its way. The parallelism enhances the likelihood seeking the optimal solution via the collaboration among the HGAs.

• ETRI Journal
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• v.43 no.2
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• pp.260-271
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• 2021

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies. The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N2).

• Proceedings of the KIEE Conference
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• summer
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• pp.36-38
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• 2004

This paper presents an application of parallel hybrid Genetic Algorithm-Tabu Search (GA-TS) algorithm to search an optimal solution of a recokiguration in distribution system. In parallel hybrid CA-TS, after CA operations, stings which are not emerged in the past population are selected in the reproduction procedure. After reproduction operation, if there are many strings which are in the past population, we add new random strings into the population, if there's no improvement for the predetermined iteration, local search procedure is executed by TS for the strings with high fitness function value. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a distribution system in the reference paper.

• Proceedings of the KIEE Conference
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• summer
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• pp.446-448
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• 2004

This paper presents an application of parallel hybrid Genetic Algorithm-Tabu Search (GA-TS) algorithm to search an optimal solution of a service restoration in distribution system. In parallel hybrid GA-TS, after GA operations, strings which are not emerged in the past population are selected in the reproduction procedure. After reproduction operation, if there are many strings which are in the past population, we add new random strings into the population. If there's no improvement for the predetermined iteration, local search procedure is executed by f for the strings with high fitness function value. To show the usefulness of the proposed method, developed algorithm has been tested and compared on a practical distribution system in Korea.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.1-8
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• 2023

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as "makespan". This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator's experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1624-1627
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• 1997

The 6-DOF parallel manipulator is a closed-kindmatic chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. Because of its advantage, the parallel manipulator have been widely used in many engineering applications such as vehicle/flight driving simulators, rogot maniplators, attachment tool of machining centers, etc. However, the kinematic analysis for the implementation of a real-time controller has some problem because of the lack of an efficient lagorithm for solving its highly nonliner forward kinematic equation, which provides the translational and orientational attitudes of the moveable upper platform from the lenght of manipulator linkages. Generally, Newton-Raphson method has been widely sued to solve the forward kinematic problem but the effectiveness of this methodology depend on how to set initial values. This paper proposes a hybrid method using genetic algorithm(GA) and Newton-Raphson method to solve forward kinematics. That is, the initial values of forward kinematics solution are determined by adopting genetic algorithm which can search grobally optimal solutions. Since determining this values, the determined values are used in Newton-Raphson method for real time calcuation.