Spiking neural P ant optimisation: a novel approach for ant colony optimisation

Niching for Ant Colony Optimisation

Evolutionary Computation niching methods, such as Fitness Sharing and Crowding, are aimed at simultaneously locating and maintaining multiple optima to increase search robustness, typically in multi-modal function optimization. Such methods have been shown to be useful for both single and multiple objective optimisation problems. Niching methods have been adapted in recent years for other optim...

Candidate Set Strategies for Ant Colony Optimisation

Ant Colony Optimisation based solvers systematically scan the set of possible solution elements before choosing a particular one. Hence, the computational time required for each step of the algorithm can be large. One way to overcome this is to limit the number of element choices to a sensible subset, or candidate set. This paper describes some novel generic candidate set strategies and tests t...

Ant Colony Optimisation for Job Shop Scheduling

A recent adaptive algorithm, named Ant System, is introduced and used to solve the problem of job shop scheduling. The algorithm was first introduced by Dorigo, Maniezzo and Colorni in 1991 and is derived from the foraging and recruiting behaviour observed in an ant colony. It can be applied to combinatorial optimisation problems. This paper outlines the algorithm’s implementation and performan...

Model Checking the Ant Colony Optimisation

We present a model for the travelling salesman problem (TSP) solved using the ant colony optimisation (ACO), a bio-inspired mechanism that helps speed up the search for a solution and that can be applied to many other problems. The natural complexity of the TSP combined with the selforganisation and emergent behaviours that result from the application of the ACO make model-checking this system ...

Ant Colony Optimisation: Algorithms and Applications