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Search Results: 1 - 10 of 15356 matches for " Ant Colony Search Algorithm "
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Iwan Halim Sahputra,Tanti Octavia,Agus Susanto Chandra
Jurnal Teknik Industri , 2009,
Abstract: Ant colony optimization (ACO) is one of the meta-heuristic methods developed for finding solutions to optimization problems such as scheduling. Local search method is one part of the ACO which determines the quality of the resulting solution. In this paper, Tabu Search was proposed as a method of local search in ACO to solve the problem of flowshop scheduling. The purpose of this scheduling was to minimize the makespan. Makespan and computation time of the proposed method were compared to the ACO that implemented Job-Index as local search method. Using proposed algorithm, makespan values obtained were not significantly different than solutions of ACO using Job-Index method, and had computation time shorter. Abstract in Bahasa Indonesia: Ant colony optimization (ACO) adalah salah satu metode meta-heuristic yang dikembangkan untuk mencari solusi bagi permasalahan optimasi seperti penjadwalan. Metode local search merupakan salah satu bagian dari ACO yang menentukan kualitas solusi yang dihasilkan. Dalam makalah ini Tabu Search diusulkan sebagai metode local search dalam algoritma ACO untuk menyelesaikan masalah penjadwalan flowshop. Tujuan dari penjadwalan ini adalah untuk meminimalkan makespan. Hasil makespan dan computation time dari metode usulan ini akan dibandingkan dengan algoritma ACO yang menggunakan Job-Index sebagai metode local search. Dengan menggunakan algoritma Tabu Search sebagai local search didapat nilai makespan yang tidak berbeda secara signifikan dibandingkan yang menggunakan metode Job-Index, dengan kelebihan computation time yang lebih singkat. Kata kunci: Tabu Search, Ant Colony Algorithm, Local Search, Penjadwalan Flowshop.
Ant Colony Search Algorithm for Solving Unit Commitment Problem
K. Lenin,B.Ravindranath Reddy,M.Surya Kalavathi
International Journal of Mechatronics, Electrical and Computer Technology , 2013,
Abstract: In this paper Ant Colony Search Algorithm is proposed to solve thermal unit commitment problem. Ant colony search (ACS) studies are inspired from the behavior of real ant colonies that are used to solve function or combinatorial optimization problems. In the ACSA a set of cooperating agents called ants cooperates to find good solution of unit commitment problem of thermal units. The UC problem is to determine a minimal cost turn-on and turn-off schedule of a set of electrical power generating units to meet a load demand while satisfying a set of operational constraints. This proposed approach is a tested on 10 unit power system and compared to conventional methods.
An Ant Colony Algorithm for the Flowshop Scheduling Problem
S.J. Sadjadi,J.L. Bouquard,M. Ziaee
Journal of Applied Sciences , 2008,
Abstract: In this study, we considered the flowshop scheduling problem with the objectives of the makespan (F//Cmax) and the total flowtime (F//Σfi) separately. The permutation case of the problem was first solved by an Ant Colony Optimization (ACO) algorithm. The permutation solutions of this ACO algorithm were then improved by a non-permutation local search. In order to evaluate the performance of the proposed metaheuristic, computational experiments were performed using the well-known benchmark problems. A comparison with Rajendran solutions and the best metaheuristic solutions known for Taillard benchmark problems was carried out, show that the proposed ACO algorithm was clearly superior to the above metaheuristics.
Ant colony search algorithm for optimal reactive power optimization
Lenin K.,Mohan M.R.
Serbian Journal of Electrical Engineering , 2006, DOI: 10.2298/sjee0601077l
Abstract: The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (AGA).
Intelligent Search Study Based on Improved Ant Colony Algorithm in P2P Networks

苏锦旗, 郭玉龙
Hans Journal of Data Mining (HJDM) , 2014, DOI: 10.12677/HJDM.2014.43003
In order to enhance the practicality of ant colony algorithm and improve the search efficiency of peer-to-peer networks, this paper presents a new approach of unstructured P2P information re-trieval based on the polymorphic ant colony algorithm. In order to meet the new file requirement after a while of searching, the conception of generated pheromone is imported to decrease the blindness of pack forwarding in early searching stage. Based on the simulator framework, simu-lating the flooding, ant colony algorithm, ant colony algorithm with generated pheromone in un-structured peer-to-peer networks, and analyzing the experience data, the experience results indi-cate that the algorithm is effective and can enhance the performance of peer-to-peer networks.

Ant Colony with Genetic Algorithm Based on Planar Graph for Multiple Sequence Alignment
Xuyu Xiang,Dafan Zhang,Jiaohua Qin,Yuanyuan Fu
Information Technology Journal , 2010,
Abstract: Multiple Sequence Alignment (MSA), known as NP-complete problem, is among the most important and challenging tasks in computational biology. For multiple sequence alignment, it is difficult to solve this type of problems directly and always results in exponential complexity. In order to effectively solve the MSA problem, in this study, we present a novel algorithm of ant colony with genetic algorithm (ACG) based on the planar graph representation for MSA. Firstly, the planar graph is described a representation for multiple sequences that took every possible aligning result into account by defining the representation of gap insertion, the value of heuristic information in every optional path and scoring rule for the processes of MSA. Secondly, we use an ant colony with genetic algorithm to find the better path that denotes a better aligning result for multidimensional graph. Experimental results show that ACG could bring about a rise in the quality of MSA when compared with standard Clustal algorithm.
Advances of Soft Computing Methods in Edge Detection
Amir Atapour Abarghouei,Afshin Ghanizadeh,Siti Mariyam Shamsuddin
International Journal of Advances in Soft Computing and Its Applications , 2009,
Abstract: Artificial Intelligence (AI) techniques are now commonly used tosolve complex and ill-defined problems. AI a broad field and willbring different meanings for different people. John McCarthy wouldprobably use AI as “computational intelligence”, while Zadehclaimed that computational intelligence is actually Soft Computing(SC) techniques. Regardless of its definition, AI concerns with tasksthat require human intelligence which require complex andadvanced reasoning processes and knowledge. Due to its ability tolearn, handle incomplete or incomprehensible data, deal with nonlinearproblems, and perform reasonable tasks very fast, AI has beenused in diverse applications in control, robotics, pattern recognition,forecasting, medicine, power systems, manufacturing, optimization,signal processing, and social sciences. However, in this paper, wewill focus on Soft Computing (SC), one of the AI influences thatsprang from the concept of cybernetics. The main objective of thispaper is to illustrate how some of these SC techniques generallywork on detecting the edges. The paper also outlines practicaldifferences among these techniques when they are applied to solvingthe problem of edge detection.
Ant colony pattern search algorithms and their convergence

FENG Yuan-jing,YU Li,FENG Zu-ren,

控制理论与应用 , 2007,
Abstract: A class of ant colony pattern search algorithms (ACPSAs) are designed for the optimization of multimodal functions in continuous space. ACPSAs guide the individuals to perform region searches by objective function heuristic pheromone. Further local searches are handled by pattern searches of individuals, then the search results are shared with pheromone fusion, providing the basis for the region searches in the next iteration. The probabilistic convergence theories of ACPSAs are also given by stochastic pattern search algorithm theory. APCSAs present interesting emergent properties as shown by some analytical test functions. Finally, the comparison results with typical stochastic optimization algorithms show the effectiveness of the algorithms and the advantage in swarm cooperation.
Searching Experience Sharing Based on Ant Colony Model  [PDF]
Jung-Sing Jwo, Chung-Ren Nian
Journal of Software Engineering and Applications (JSEA) , 2012, DOI: 10.4236/jsea.2012.59075
Abstract: Search engine is an important tool to all the Internet users. It helps users finding useful contents in the cyberspace. However, searching experiences among different users are difficult to be shared and accumulated. In this paper, a concept called search-trail is proposed. Based on ant colony model, search-trails are created from the searching steps to the target contents. The search-trails built from various users are very similar to the trails generated in an ant colony. The simulations of the proposed solution demonstrate that even in the case of few searching experienced users, the generated search-trails still possess 96.29% similarity to the expected ones in 60 days. It shows that the concept of search-trails can really help users accumulating, sharing and reusing their search experiences.
A Survey of Algorithms for Paper-reviewer Assignment Problem
Kolasa Tomasz,Krol Dariusz
IETE Technical Review , 2011,
Abstract: This paper analyzes some details of artificial in-telligence algorithms to paper-reviewer assignment problem. In particular, the study on most common algorithms like genetic algorithm (GA), ant colony optimization (ACO) and tabu-search (TS) is made and the performance of these algorithms in paper-reviewer assignment problem is tested. Moreover, two hybrid methods that efficiently combine the above-mentioned algorithms are proposed: the ACO-GA and GA-TS algorithms. To measure the performance of these algorithms, extensive computational experiments are conducted. Evaluation using different data sets shows that the proposed algorithms are effective and achieve good results.
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