Job shop scheduling using ACO(Ant Colony
Optimization) approach. Different heuristic information
is discussed and three different ant algorithms are
presented. State transition rule and pheromone updating
methods are given. The concept of the new strategy is
highlighted and template for ACO approach is presented.
Published In : IJCAT Journal Volume 2, Issue 8
Date of Publication : August 2015
Pages : 315 - 323
Figures :4
Tables : --
Publication Link :Job Shop Scheduling Using ACO
Ms. K. Sathya Sundari : (Ph. D., Part time Category – B Research & Development Centre, Bharathiar University, Coimbatore)
Tamil Nadu, India
This chapter has provided meta-heuristic approach
called ant colony optimization. Behavior of ants to find
shortest path has been given. Different ant algorithms
have been discussed together with local and global
pheromone updating. The key to the application of
ACO to a new problem is to identify an appropriate
representation for the problem (to be represented as a
graph searched by many artificial ants), and an
appropriate heuristic that defines the distance between
any two nodes of the graph. Then the probabilistic
interaction among the artificial ants mediated by the
pheromone trail deposited on the graph edges will
generate good, and often optimal, problem solutions.
Other problems solved by ACO algorithms include:
graph partitioning; subset problems including knapsack
problems; Quadratic assignment; graph colouring;
vehicle routing; networking routing and many more.
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Optimization. MIT Press, Cambridge, MA, 2004.
