Modifying Ant Colony Optimization

Nonsiri, S.; Supratid, Siriporn

doi:10.1109/smcia.2008.5045942

Cited by 15 publications

(6 citation statements)

References 8 publications

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“…Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for the problem instance under concern, by exploiting artificial pheromone information that is adapted based on the ant search experience and possibly available heuristic information [30]. ACO was successfully applied to many problems such as the traveling salesman problem [31][32][33][34] and mentioned earlier job shop scheduling [26][27][28][29].…”

Section: Literature Reviewmentioning

confidence: 99%

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

Korytkowski

Rymaszewski

Wiśniewski

2013

Int J Adv Manuf Technol

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This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multiattribute dispatching rule assignment.

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Section: Literature Reviewmentioning

confidence: 99%

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

Korytkowski

Rymaszewski

Wiśniewski

2013

Int J Adv Manuf Technol

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“…They also described about Underload and Overload of load balancing methods. Sarayut Nonsiri and Siriporn Supratid [3] discussed about the ACO that allows fast near optimal solutions to be found. It is useful in industrial environments where computational resources and time are limited.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing Using ANT Colony in Cloud Computing

Kumar¹,

Sahoo²

2013

IJITCS

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“…We have experimented with several values for the term ∆ (r,s).A good choice was inspired by Q-learning [3] , an algorithm developed to solve reinforcement learning problems which allows an agent to learn such an optimal policy by the recursive application of a rule. ∆ (r,s)=γ.max (s,z) where zєJ k (s) and 0<γ<=1.Alternate choices may be ∆ (r,s)=t 0 or ∆ (r,s)=0.…”

Section: Acs Local Updating Rulementioning

confidence: 99%

“…The increased amount of pheromone attributed as the positive feedback. This optimization technique does not rely on mathematical description of the specific issues, but has strong global optimization feature [3], high performance [4] and flexibility. Three main aspects to determine ACS are:…”

Section: Introductionmentioning

confidence: 99%

An Improved Modular Hybrid Ant Colony Approach for Solving Traveling Salesman Problem

Sahana¹,

Jain²

2011

JoC

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Our primary aim is to design a framework to solve the well known traveling salesman problem(TSP) using combined approach of Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Several solutions exists for the above problem using ACO or GA and even using a hybrid approach of ACO and GA. Our framework gives the optimal solution for the above problem by using the modular hybrid approach of ACO and GA along with heuristic approaches.We have incorporated GA, RemoveSharp and LocalOpt heuristic approaches in ACO module, hence each iteration calls the GA and heuristics within ACO module which results in a higher amount of pheromone deposited in the optimal path for global pheromone update. As a result the convergence is quicker and solution is optimal.

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Modifying Ant Colony Optimization

Cited by 15 publications

References 8 publications

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

Ant colony optimization for job shop scheduling using multi-attribute dispatching rules

Load Balancing Using ANT Colony in Cloud Computing

An Improved Modular Hybrid Ant Colony Approach for Solving Traveling Salesman Problem

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