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.

Modified hybrid vision correction algorithm을 활용한 상수관망 최적설계

류용민,이의훈 한국수자원학회 2022 한국수자원학회논문집 Vol.55 No.-

The optimal design of Water Distribution System (WDS) is used in various ways according to the purpose set by the user. The optimal design of WDS has various purposes, such as minimizing costs and minimizing energy generated when manufacturing pipes. In this study, based on the Modified Hybrid Vision Correction Algorithm (MHVCA), a cost-optimal design was conducted for various WDSs. We also propose a new evaluation index, Best Rate (BR). BR is an evaluation index developed based on the K-mean Clustering Algorithm. Through BR, a comparison was made on the possibility of searching for the optimal design of each algorithm used in the optimal design of WDS. The results of MHVCA for WDS were compared with Vision Correction Algorithm (VCA) and Hybrid Vision Correction Algorithm (HVCA). MHVCA showed a lower cost design than VCA and HVCA. In addition, MHVCA showed better probability of lower cost designs than VCA and HVCA. MHVCA will be able to show good results when applied to the optimal design of WDS for various purposes as well as the optimal design of WDS for cost minimization applied in this study. 상수관망 최적설계는 사용자가 설정한 목적에 따라 다양하게 사용된다. 상수관망 최적설계는 비용의 최소화 및 관의 제작 시 발생하는 에너지 최소화 등 목적이 다양하게 존재한다. 본 연구에서는 Modified Hybrid Vision Correction Algorithm (MHVCA)을 기반으로 다양한 상수관망에 대한 비용 최적설계를 진행하였다. 또한 새로운 평가지표인 Best Rate (BR)를 제안하였다. BR은 K-mean Clustering Algorithm을 기반으로 개발된 평가지표이다. BR을 통해 상수관망 최적설계에 사용된 각 알고리즘의 최적 설계안 탐색 가능성에 대한 비교를 하였다. 다양한 관망에 대한 MHVCA의 최적설계 결과를 Vision Correction Algorithm (VCA) 및 Hybrid Vision Correction Algorithm (HVCA)과 비교하였다. MHVCA는 VCA 및 HVCA보다 낮은 비용의 설계안을 탐색하였다. 또한 MHVCA는 낮은 비용의 설계안을 탐색할 확률이 VCA 및 HVCA보다 높았다. MHVCA는 본 연구에서 적용한 비용 최소화를 위한 상수관망 최적설계 뿐만이 아닌 다양한 목적을 위한 상수관망 최적설계에 적용할 경우 좋은 결과를 나타낼 수 있을 것이다.

A New Clustering Algorithm of Hybrid Strategy Optimization

Li Yi-ran,Zhang Chun-na 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.7

Normally, improving the performance of clustering depends on improvement of the algorithm. On the basis, this paper presents a hybrid strategy optimization algorithm that K-means algorithm effectively combined with PSO algorithm, which not only has played their respective advantages, but also reflected a hybrid performance. First of all, combined with a semi-supervised clustering idea, to optimize the clustering center of particle by K - means in the iteration of algorithm, enhanced the searching capability of the particles. Secondly, improved the traditional K - means enhance the ability of the algorithm to deal with the concave and convex points. Finally, the algorithm is introduced into the particle state determination mechanism, on implementing mutation for unstable particles, so that the algorithm to obtain stable performance. Experimental results show that the hybrid algorithm optimization ability is outstanding, and the convergence and stability can be effectively improved.

An Integer Program and a Hybrid Genetic Algorithm for the University Timetabling Problem

Yuna Lee,Ilkyeong Moon 대한산업공학회 2014 대한산업공학회 추계학술대회논문집 Vol.2014 No.11

The university timetabling problem (UTP) has been studied by numerous research groups for decades. The studies were conducted through various methods including linear algorithms, mathematical models, heuristics, and metaheuristics. In this paper, an integer program, a heuristic algorithm, and a hybrid genetic algorithm are used to solve the UTP characterized by real-world constraints such as periodicity and consecutiveness. The integer program can be used to efficiently alter constraints and improve accuracy. However, it is inefficient with large problems, because the UTP is NP-hard problem, so a heuristic algorithm with left upper strategy and a hybrid genetic algorithm were developed. The first experiment showed the effects of the periodicity and consecutiveness constraints ratio and the second experiment compared the performances of the heuristic and hybrid genetic algorithms with small, medium, and large problems. The integer program was coded in FICO Xpress-IVE version 7.3, and the heuristic and the hybrid genetic algorithm were implemented in Java programming language. The results illustrate that the higher ratio of the lectures with consecutiveness constraints deducted the better objectives. For larger problems, the IP could not reach the optimum within 7200 seconds, and the hybrid genetic algorithm yielded better solutions than the heuristic algorithm.

A hybrid optimization algorithm to explore atomic configurations of TiO₂ nanoparticles

Inclan, Eric,Geohegan, David,Yoon, Mina Elsevier 2018 Computational materials science Vol.141 No.-

<P><B>Abstract</B></P> <P>In this paper we present a hybrid algorithm comprised of differential evolution, coupled with the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton optimization algorithm, for the purpose of identifying a broad range of (meta)stable Ti<I> <SUB>n</SUB> </I>O<SUB>2</SUB> <I> <SUB>n</SUB> </I> nanoparticles, asan example system, described by Buckingham interatomic potential. The potential and its gradient are modified to be piece-wise continuous to enable use of these continuous-domain, unconstrained algorithms, thereby improving compatibility. To measure computational effectiveness a regression on known structures is used. This approach defines effectiveness as the ability of an algorithm to produce a set of structures whose energy distribution follows the regression as the number of Ti<I> <SUB>n</SUB> </I>O<SUB>2</SUB> <I> <SUB>n</SUB> </I> increases such that the shape of the distribution is consistent with the algorithm’s stated goals. Our calculation demonstrates that the hybrid algorithm finds global minimum configurations more effectively than the differential evolution algorithms, widely employed in the field of materials science. Specifically, the hybrid algorithm is shown to reproduce the global minimum energy structures reported in the literature up to <I>n</I> =5, and retains good agreement with the regression up to <I>n</I> =25. For 25< <I>n</I> <100, where literature structures are unavailable, the hybrid effectively obtains structures that are in lower energies per TiO<SUB>2</SUB> unit as the system size increases.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

항행 및 항법 : 공항 지상이동 경로 탐색을 위한 실용 알고리즘 개발

윤석재 ( Seok Jae Yun ),구성관 ( Sung Kwan Ku ),백호종 ( Ho Jong Baik ) 한국항행학회 2015 韓國航行學會論文誌 Vol.19 No.2

지속적으로 증가하고 있는 항공수요에 따라, 공항운영 측면에서 이동지역 내 항공기 이동에 대한 효율성을 증대할 수 있는 방안의 중요성이 대두되고 있다. 본 논문은 공항 이동지역을 운항하는 항공기에게 최단경로를 적시에 제공하여 공항운영의 효율성을 증대시키기 위한 경로 탐색 알고리즘을 제시하고자 한다. 기존 문헌들에서 여러 알고리즘이 개발되었는데, 대표적으로 Dijkstra 알고리즘 A* 알고리즘이 있다. Dijkstra 알고리즘은 상대적으로 느린 연산속도로 인해 공항구조가 복합해질 경우 최단경로를 적시에 제공하기 어려울 수 있다는 단점이 있으며, A* 알고리즘은 최적성을 보장하지 못한다는 단점이 있다. 본 논문에서는 두 알고리즘을 병합하여, 각 알고리즘의 단점을 보완한 새로운 Hybrid A* 알고리즘을 제시하였다. 성능분석 결과, Hybrid A* 알고리즘은 경로 탐색에 있어 빠른 연산속도와 최적성이 개선됨을 확인하였다. Motivated by continuous increase in flight demand, awareness of the importance in developing ways to increase aircraft operational efficiency on the airport movement area has been raised. This paper proposes a new routing algorithm for providing the shortest path in a right time, enhancing the aircraft movement efficiency. Many researches on developing algorithms have been performed, for example, Dijkstra algorithm and A* algorithm. The Dijkstra algorithm provide optimal solution but could possibly provide it with a cost of relatively longer computation time. On the other hand, A* algorithm does not guarantee the optimality of a solution. In this paper, we suggest a Hybrid A* algorithm, incorporating both algorithms to eliminate the weaknesses. Rigorous test shows the proposed Hybrid A* algorithm may achieve shorter computing time and optimality in searching the shortest path.

A Novel Hybrid Optimization Algorithm and its Application in Solving Complex Problem

Hao Jia 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.2

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) algorithm based on combining collaborative strategy, particle swarm optimization and ant colony optimization is proposed for the traveling salesman problems in this paper. The HPSACO algorithm makes use of the exploration capability of the PSO algorithm and stochastic capability of the ACO algorithm. The main idea of the HPSACO algorithm uses the rapidity of the PSO algorithm to obtain a series of initializing optimal solutions for dynamically adjusting the initial pheromone distribution of the ACO algorithm. Then the parallel search ability of the he ACO algorithm are used to obtain the optimal solution of solving problem. Finally, various scale TSP are selected to verify the effectiveness and efficiency of the proposed HPSACO algorithm. The simulation results show that the proposed HPSACO algorithm takes on the better search precision, the faster convergence speed and avoids the stagnation phenomena.

피드포워드와 피드백 알고리즘의 연성을 줄이는 새로운 하이브리드 능동소음제어 알고리즘

정익채(Ik-Chae Jeong),박영진(Young-Jin Park) 대한기계학회 2016 대한기계학회 춘추학술대회 Vol.2016 No.12

Passive noise control is simple and effective way to reduce noise. But passive noise control is not effective at low frequency. So ANC(active noise control) is used to use to solve this problem. The algorithm of ANC is mostly used FXLMS(Filtered-X LMS) algorithm. Feedforward FXLMS algorithm suffers from low correation with reference signal and noise signal. Feedback ANC algorithm suffers from causality. To complement weakness of two algorithms, hybrid ANC algorithm is used. Conventional hybrid ANC algorithm also suffers from coupling of two algorithm that can cause destabilization. So, this paper Proposes new hybrid ANC algorithm that reduces each algorithm’s destabilization.

개선된 물순환 최적화 알고리즘을 이용한 하이브리드 전자석 설계

조재훈(Jae-Hoon Cho),김용태(Yong-Tae Kim) 한국지능시스템학회 2019 한국지능시스템학회논문지 Vol.29 No.3

본 논문에서는 개선된 물순환 최적화 알고리즘을 이용한 하이브리드 전자석의 최적 설계 기법을 제안한다. 하이브리드 전자석 기반의 자기부상 시스템은 전자석만을 사용하는 부상 시스템에 비해 저전력으로 동작할 수 있는 장점이 있다. 제안된 알고리즘은 지능형 최적화 기법 중 물순환 최적화 알고리즘과 클론선택을 이용하였으며 물순환 알고리즘은 전역적인 탐색을 수행하고, 클론선택은 최적해의 근처에서 지역탐색을 수행한다. 제안된 알고리즘의 이러한 특성은 일반적인 지능형 최적화 알고리즘에서의 지역해의 조기수렴 문제를 해결할 수 있다. 하이브리드 전자석 설계를 위하여 전력소모와 부상력을 동시에 만족하는 비용함수를 사용하여 모의실험을 수행하였다. 실험결과에서 제안된 알고리즘의 기존의 일반적인 기법과 지능형 최적화 알고리즘에 비해 우수한 성능을 보이는 것을 확인할 수 있었다. In this paper, we propose an optimal design method of a hybrid electromagnet using an improved water circulation algorithm. The hybrid electromagnet based magnetic levitation system has an advantage in that it can operate only at a low power compared to a levitation system using only an electromagnet. The proposed algorithm uses the water cycle algorithm(WCA) and the clonal selection(CS) in the intelligent optimization method. The WCA performs the global search and the CS performs the local search in the vicinity of the optimal solution. This characteristic of the proposed algorithm can solve the problem of premature convergence in general intelligent optimization algorithm. For the design of the hybrid magnet, we proposed a cost function that simultaneously satisfied the power consumption and the levitation force. Experimental results show that the proposed algorithm shows better performance than the conventional algorithm and the optimization algorithm.

Fast Convergence and Improved Particle Swarm Hybrid Optimization Algorithm

Li Yi-ran,Zhang Chun-na 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.8

Aiming at the problem that the particle in the traditional particle swarm optimization algorithm is easy to fall into the local optimum and the convergence rate is slow, this paper proposed an improved particle swarm optimization algorithm. In particle swarm optimization algorithm, the advantages and disadvantages of the algorithm is directly decided by the performance of the particle, the paper introduced the chaos mechanism, enhance the ergodicity and particle will be quantized in the solution space, on the premise of ensuring diversity of solution, the particle get better global search ability. Meanwhile, based on the problem of slow convergence speed of the algorithm in the late, on the one hand to dynamically adjust the inertia weight of impact speed, makes the particle movement speed tend to be reasonable, on the other hand, using k-means algorithm to optimize progeny particle and get more reasonable clustering center, make the algorithm fast convergence. Experiments show that using improved Particle Swarm Optimization algorithm with high precision, strong stability and fast convergence.