• 제목/요약/키워드: Local Optimization Algorithm

검색결과 482건 처리시간 0.037초

Water Flowing and Shaking Optimization

  • Jung, Sung-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제12권2호
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    • pp.173-180
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    • 2012
  • This paper proposes a novel optimization algorithm inspired by water flowing and shaking behaviors in a vessel. Water drops in our algorithm flow to the gradient descent direction and are sometimes shaken for getting out of local optimum areas when most water drops fall in local optimum areas. These flowing and shaking operations allow our algorithm to quickly approach to the global optimum without staying in local optimum areas. We experimented our algorithm with four function optimization problems and compared its results with those of particle swarm optimization. Experimental results showed that our algorithm is superior to the particle swarm optimization algorithm in terms of the speed and success ratio of finding the global optimum.

A Novel Hybrid Intelligence Algorithm for Solving Combinatorial Optimization Problems

  • Deng, Wu;Chen, Han;Li, He
    • Journal of Computing Science and Engineering
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    • 제8권4호
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    • pp.199-206
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    • 2014
  • The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

전역 및 국소 최적화탐색을 위한 향상된 유전 알고리듬의 제안 (An Enhanced Genetic Algorithm for Global and Local Optimization Search)

  • 김영찬;양보석
    • 대한기계학회논문집A
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    • 제26권6호
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    • pp.1008-1015
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    • 2002
  • This paper proposes a combinatorial method to compute the global and local solutions of optimization problem. The present hybrid algorithm is the synthesis of a genetic algorithm and a local concentrate search algorithm (simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm, but also gives a more accurate solution. In addition, this algorithm can find both the global and local optimum solutions. An optimization result is presented to demonstrate that the proposed approach successfully focuses on the advantages of global and local searches. Three numerical examples are also presented in this paper to compare with conventional methods.

이산설계공간에서 직교배열표를 이용한 순차적 알고리듬의 국부해 (Local Solution of Sequential Algorithm Using Orthogonal Arrays in Discrete Design Space)

  • 이정욱;박경진
    • 대한기계학회:학술대회논문집
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    • 대한기계학회 2004년도 춘계학술대회
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    • pp.1005-1010
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    • 2004
  • The structural optimization has been carried out in the continuous design space or in the discrete design space. Generally, available designs are discrete in design practice. But methods for discrete variables are extremely expensive in computational cost. In order to overcome this weakness, an iterative optimization algorithm was proposed for design in the discrete space, which is called as a sequential algorithm using orthogonal arrays (SOA). We focus to verify the fact that the local solution can be obtained throughout the optimization with this algorithm. The local solution is defined in discrete design space. Then the search space, which is the set of candidate values of each design variables formed by the neighborhood of current design point, is defined. It is verified that a local solution can be founded by moving sequentially the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained using the SOA algorithm

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Multi-objective optimization of foundation using global-local gravitational search algorithm

  • Khajehzadeh, Mohammad;Taha, Mohd Raihan;Eslami, Mahdiyeh
    • Structural Engineering and Mechanics
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    • 제50권3호
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    • pp.257-273
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    • 2014
  • This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and $CO_2$ emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded $CO_2$ emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

A New Approach to System Identification Using Hybrid Genetic Algorithm

  • Kim, Jong-Wook;Kim, Sang-Woo
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2001년도 ICCAS
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    • pp.107.6-107
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    • 2001
  • Genetic alogorithm(GA) is a well-known global optimization algorithm. However, as the searching bounds grow wider., performance of local optimization deteriorates. In this paper, we propose a hybrid algorithm which integrates the gradient algorithm and GA so as to reinforce the performance of local optimization. We apply this algorithm to the system identification of second order RLC circuit. Identification results show that the proposed algorithm gets the better and robust performance to find the exact values of RLC elements.

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The Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator

  • Li, Lixian;Lee, Jaewan
    • 인터넷정보학회논문지
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    • 제21권3호
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    • pp.21-28
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    • 2020
  • Fireworks Algorithm (FWA) is a relatively novel swarm-based metaheuristic algorithm for global optimization. To solve the low-efficient local searching problem and convergence of the FWA, this paper presents a Variable Amplitude Coefficient Fireworks Algorithm with Uniform Local Search Operator (namely VACUFWA). Firstly, the explosive amplitude is used to adjust improving the convergence speed dynamically. Secondly, Uniform Local Search (ULS) enhances exploitation capability of the FWA. Finally, the ULS and Variable Amplitude Coefficient operator are used in the VACUFWA. The comprehensive experiment carried out on 13 benchmark functions. Its results indicate that the performance of VACUFWA is significantly improved compared with the FWA, Differential Evolution, and Particle Swarm Optimization.

순회 판매원 문제 해결을 위한 개미집단 최적화 알고리즘 개선 (Improvement of Ant Colony Optimization Algorithm to Solve Traveling Salesman Problem)

  • 장주영;김민제;이종환
    • 산업경영시스템학회지
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    • 제42권3호
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    • pp.1-7
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    • 2019
  • It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

이산설계공간에서 직교배열표를 이용한 순차적 알고리듬의 국부해 (Local Solution of a Sequential Algorithm Using Orthogonal Arrays in a Discrete Design Space)

  • 이정욱;박경진
    • 대한기계학회논문집A
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    • 제28권9호
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    • pp.1399-1407
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    • 2004
  • Structural optimization has been carried out in continuous design space or in discrete design space. Generally, available designs are discrete in design practice. However, the methods for discrete variables are extremely expensive in computational cost. An iterative optimization algorithm is proposed for design in a discrete space, which is called a sequential algorithm using orthogonal arrays (SOA). We demonstrate verifying the fact that a local optimum solution can be obtained from the process with this algorithm. The local optimum solution is defined in a discrete design space. Then the search space, which is a set of candidate values of each design variables formed by the neighborhood of a current design point, is defined. It is verified that a local optimum solution can be found by sequentially moving the search space. The SOA algorithm has been applied to problems such as truss type structures. Then it is confirmed that a local solution can be obtained by using the SOA algorithm

유사성 계수를 이용한 군집화 문제에서 유전자와 국부 최적화 알고리듬의 적용 (Application of Genetic and Local Optimization Algorithms for Object Clustering Problem with Similarity Coefficients)

  • 임동순;오현승
    • 대한산업공학회지
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    • 제29권1호
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    • pp.90-99
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    • 2003
  • Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.