Joint Optimization Method Combining Genetic Algorithm and Numerical Algorithm Based on MATLAB

Yanhua Guo,Feifei Liu,Ning Zhang,Tao Wang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11

A two-bar plane truss is builtin MATLAB based on mathematical model. Then the authors use genetic algorithm toolbox to solve this problem. The parametric truss model is set up in the finite element analysis software ANSYS. It is analyzed by first-order algorithm. The comparison of two kinds results show the pure genetic algorithm doesn’t always have an advantage over other algorithms. In the end, a joint optimization method is put forward on the basis of genetic algorithm. It combines genetic algorithm based on MATLAB toolbox and numerical algorithm based on quasi-Newton method. This method is illustrated by the numerical example of the two-bar plane truss. The results show this joint optimization method can get the global optimal solution of this problem every time.

Discrete Optimum Design of Space Truss Structures Using Genetic Algorithms

Park, Choon Wook,Kang, Moon Myung Architectural Institute of Korea 2002 Architectural research Vol.4 No.1

The objective of this study is the development of discrete optimum design algorithms which is based on the genetic algorithms. The developed algorithms was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses structures and the constraints are stresses and displacements. This study solves the problem by introducing the genetic algorithms. The genetic algorithms consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed discrete optimum design algorithms was verified by applying the algorithms to optimum design examples.

Study on Traditional Flower Design Based on Genetic Algorithm Optimized by K-medoids Algorithm

Junwei Qi 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.10

With the continuous improvement of the economic level, people pay more and more attention to the appreciation of art. Art of flower arranging is an art following certain laws of creation and it has a long history. At present, the flower shape depends mainly on the arrangements’ experience and personal preferences. This paper attempts to use genetic algorithm optimized by the K-medoids algorithm for its research, through the replacement of the genetic algorithm to get more design solutions, so as to broaden the designer's ideas, to achieve the innovation of art design. Experiments show that the beauty of floral works produced by the optimized genetic algorithm is better than that produced by traditional genetic algorithm.

휴리스틱 기반의 유전 알고리즘을 활용한 경로 탐색 알고리즘

고정운(Jung-Woon Ko ),이동엽(Dong-Yeop Lee) 한국게임학회 2017 한국게임학회 논문지 Vol.17 No.5

경로 탐색 알고리즘은 이동 가능한 에이전트가 게임 내의 가상 월드에서 현재 위치로부터 목적지까지 가는 경로를 탐색하는 알고리즘을 뜻한다. 기존의 경로 탐색 알고리즘은 A*, Dijkstra와 같이 비용기반으로 그래프 탐색을 수행한다. A*와 Dijkstra는 월드 맵에서 이동 가능한 노드와 에지 정보들을 필요로 해서 맵의 정보가 다양하고 많은 온라인 게임에 적용하기 힘들다. 본 논문에서는 가변환경이나 맵의 데이터가 방대한 게임에서 적용 가능한 경로 탐색 알고리즘을 개발하기 위해 맵의 정보 없이 교배, 교차, 돌연변이, 진화 연산을 통해 해를 찾는 유전 알고리즘(Genetic Algorithm, GA)을 활용한 Heuristic-based Genetic Algorithm Path–finding(HGAP)를 제안한다. 제안하는 알고리즘은 Binary-Coded Genetic Algorithm을 기반으로 하며 목적지에 더 빨리 도달하기 위해 목적지로 가는 경로를 추정하는 휴리스틱 연산을 수행하여 경로를 탐색한다. The path-finding algorithm refers to an algorithm for navigating the route order from the current position to the destination in a virtual world in a game. The conventional path-finding algorithm performs graph search based on cost such as A-Star and Dijkstra. A-Star and Dijkstra require movable node and edge data in the world map, so it is difficult to apply online games with lots of map data. In this paper, we provide a Heuristic-based Genetic Algorithm Path-finding(HGAP) using Genetic Algorithm(GA). Genetic Algorithm is a path-finding algorithm applicable to game with variable environment and lots of map data. It seek solutions through mating, crossing, mutation and evolutionary operations without the map data. The proposed algorithm is based on Binary-Coded Genetic Algorithm and searches for a path by performing a heuristic operation that estimates a path to a destination to arrive at a destination more quickly.

Competitive Generation for Genetic Algorithms

Jung, Sung-Hoon Korean Institute of Intelligent Systems 2007 한국지능시스템학회논문지 Vol.17 No.1

A new operation termed competitive generation in the processes of genetic algorithms is proposed for accelerating the optimization speed of genetic algorithms. The competitive generation devised by considering the competition of sperms for fertilization provides a good opportunity for the genetic algorithms to approach global optimum without falling into local optimum. Experimental results with typical problems showed that the genetic algorithms with competitive generation are superior to those without the competitive generation.

Fast Optimization by Queen-bee Evolution and Derivative Evaluation in Genetic Algorithms

Jung, Sung-Hoon Korean Institute of Intelligent Systems 2005 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.5 No.4

This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

유전자 재배열을 이용한 유전자 알고리즘의 성능향상

황인재 한국융합신호처리학회 2006 융합신호처리학회 논문지 (JISPS) Vol.15 No.1

유전자 알고리즘은 공학 분야에서 필요한 여러 가지 최적화 문제에 대하여 최적에 가까운 해를 제공해주는 반복적 알고리즘으로 알려져 있다. 본 논문에서는 특정 교배방법에서 유전자의 배열순서가 적합도가 높은 스키마의 길이에 미치는 영향을 고찰하였다. 또한 이에 따른 유전자 알고리즘의 성능 변화를 두 개의 예제를 이용한 실험을 통하여 관찰하였다. 예제로 사용된 그래프 분할과 knapsack 문제를 위해 몇 가지 유전자 재배열 방법을 제시하였다. 실험결과에 따르면 유전자 재배열 방법마다 서로 다른 유전자 알고리즘 성능을 보여주었으며, 적합도가 높은 스키마의 길이를 고려한 재배열 방법이 재배열을 하지 않았을 때 보다 유전자 알고리즘의 성능을 향상시켜 주는 것을 관찰하였다. 따라서 주어진 문제에 적합한 유전자 재배열 방법을 찾는 것이 대단히 중요함을 확인하였다. Genetic Algorithms have been known to provide near optimal solutions for various optimization problems in engineering. In this paper, we study the effect of gene order in genetic algorithms on the defining length of the schema with high fitness values. Its effect on the performance of genetic algorithms was also analyzed through two well known problems. A few gene reordering methods were proposed for graph partitioning and knapsack problems. Experimental results showed that genetic algorithms with gene reordering could find solutions of better qualities compared to the ones without gene reordering. It is very important to find proper reordering method for a given problem to improve the performance of genetic algorithms.

A Novel Hybrid Optimization Algorithm Based on GA and ACO for Solving Complex Problem

Bin Gao,Jing-Hua Zhu,Wen-chang Lang 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.8

In allusion to the deficiencies of the ant colony optimization algorithm for solving the complex problem, the genetic algorithm is introduced into the ant colony optimization algorithm in order to propose a novel hybrid optimization (NHGACO) algorithm in this paper. In the NHGACO algorithm, the genetic algorithm is used to update the global optimal solution and the ant colony optimization algorithm is used to dynamically balance the global search ability and local search ability in order to improve the convergence speed. Finally, some complex benchmark functions are selected to prove the validity of the proposed NHGACO algorithm. The experiment results show that the proposed NHGACO algorithm can obtain the global optimal solution and avoid the phenomena of the stagnation, and take on the fast convergence and the better robustness.

Competitive Generation for Genetic Algorithms

Sung Hoon Jung 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.1

A new operation termed competitive generation in the processes of genetic algorithms is proposed for accelerating the optimization speed of genetic algorithms. The competitive generation devised by considering the competition of sperms for fertilization provides a good opportunity for the genetic algorithms to approach global optimum without falling into local optimum. Experimental results with typical problems showed that the genetic algorithms with competitive generation are superior to those without the competitive generation.

유전자 알고리즘을 이용한 구형 마이크로스트립 안테나 설계

서호진,홍성욱,김흥수 濟州大學校 情報通信硏究所 1998 情報通信硏究所論文集 Vol.1 No.-

This paper presents how to optimize rectangular microstrip antennas using genetic algorithms. Genetic algorithms are global numerical-optimization methods. patterned after the natural processes of genetic recombination and evolution. Rectangular microstrip antennas is analyzed by all parameters of the improved transmission-line model. The algorithms encode each parameter which is the width. length and thickness of the patch, the width of the feed line and the thickness of the dielectric into binary sequences, called a gen. and a set of genes is a chromosome. These chromosome undergo genetic operators to arrive at the final optimal solution for the design of rectangular microstrip antennas. Genetic algorithms determine the parameters of rectangular microstrip antennas to yield the maximum antenna gains. Simulation results obtain antenna gains of 5.3dB. The genetic algorithm proves to be better than general algorithms