Optimal Selection of Parameters for Nonuniform Embedding of Chaotic Time Series Using Ant Colony Optimization

2013 ◽  
Vol 43 (2) ◽  
pp. 790-802 ◽  
Author(s):  
Meie Shen ◽  
Wei-Neng Chen ◽  
Jun Zhang ◽  
Henry Shu-Hung Chung ◽  
O. Kaynak
Keyword(s):  
Time Series ◽  
Ant Colony Optimization ◽  
Chaotic Time Series ◽  
Ant Colony ◽  
Optimal Selection ◽  
Selection Of

Implementasi Algoritma MAX-MIN Ant System pada Penjadwalan Mata Kuliah

2013 ◽  
Vol 5 (2) ◽  
pp. 48-53
Author(s):  
William Aprilius ◽  
Lorentzo Augustino ◽  
Ong Yeremia M. H.
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Ant System ◽  
University Course Timetabling ◽  
Alternative Approach ◽  
Ant System Algorithm ◽  
Index Terms ◽  
Course Timetabling Problem ◽  
University Course ◽  
Selection Of

University Course Timetabling Problem is a problem faced by every university, one of which is Universitas Multimedia Nusantara. Timetabling process is done by allocating time and space so that the whole associated class and course can be implemented. In this paper, the problem will be solved by using MAX-MIN Ant System Algorithm. This algorithm is an alternative approach to ant colony optimization. This algorithm uses two tables of pheromones as stigmergy, i.e. timeslot pheromone table and room pheromone table. In addition, the selection of timeslot and room is done by using the standard deviation of the value of pheromones. Testing is carried out by using 105 events, 45 timeslots, and 3 types of categories based on the number of rooms provided, i.e. large, medium, and small. In each category, testing is performed 5 times and for each testing, the data recorded is the unplace and Soft Constraint Penalty. In general, the greater the number of rooms, the smaller the unplace. Index Terms—ant colony optimization, max-min ant system, timetabling

Automated Selection of Appropriate Pheromone Representations in Ant Colony Optimization

2005 ◽  
Vol 11 (3) ◽  
pp. 269-291 ◽  
Author(s):  
James Montgomery ◽  
Marcus Randall ◽  
Tim Hendtlass
Keyword(s):  
Ant Colony Optimization ◽  
Search Space ◽  
Ant Colony ◽  
Effective Size ◽  
Problem Model ◽  
Trail Pheromones ◽  
Wide Range ◽  
Representation Of Solutions ◽  
Ant Trail ◽  
Selection Of

Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.

scholarly journals Ant Colony Optimization Using Common Social Information and Self-Memory

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yoshiki Tamura ◽  
Tomoko Sakiyama ◽  
Ikuo Arizono
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Route Selection ◽  
Ant System ◽  
Rapid Accumulation ◽  
Loss Of Diversity ◽  
To Come ◽  
The Traveling Salesman Problem ◽  
Short Time ◽  
Selection Of

Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an effective method to find a solution for the traveling salesman problem (TSP). The rank-based ant system (ASrank) has been proposed as a developed version of the fundamental model AS of ACO. In the ASrank, since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the ASrank can find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the ASrank, also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories (ASIM) aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP.

scholarly journals Multicriteria-Based Crowd Selection Using Ant Colony Optimization

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guan Wang ◽  
Farhad Ali ◽  
Jonghoon Yang ◽  
Shah Nazir ◽  
Ting Yang ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Market Place ◽  
Effective Activity ◽  
Single Feature ◽  
Proposed Model ◽  
Efficiency And Effectiveness ◽  
Selection For ◽  
Collaboration And Communication ◽  
Selection Of

Internet-enabled technologies have provided a way for people to communicate and collaborate with each other. The collaboration and communication made crowdsourcing an efficient and effective activity. Crowdsourcing is a modern paradigm that employs cheap labors (crowd) for accomplishing different types of tasks. The task is usually posted online as an open call, and members of the crowd self-select a task to be carried out. Crowdsourcing involves initiators or crowdsourcers (an entity usually a person or an organization who initiate the crowdsourcing process and seek out the ability of crowd for a task), the crowd (online participant who is a having a particular background, qualification, and experience for accomplishing task in crowdsourcing activity), crowdsourcing task (the activity in which the crowd contribute), the process (how the activity is carried out), and the crowdsourcing platform (software or market place) where requesters offer various tasks and crowd workers complete these tasks. As the crowdsourcing is carried out in the online environment, it gives rise to certain challenges. The major problem is the selection of crowd that is becoming a challenging issue with the growth in crowdsourcing popularity. Crowd selection has been significantly investigated in crowdsourcing processes. Nonetheless, it has observed that the selection is based only on a single feature of the crowd worker which was not sufficient for appropriate crowd selection. For addressing the problem of crowd selection, a novel “ant colony optimization-based crowd selection method” (ACO-CS) is presented in this paper that selects a crowd worker based on multicriteria features. By utilizing the proposed model, the efficiency and effectiveness of crowdsourcing activity will be increased.

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