Ant Colony Optimisation

Ant Colony Optimisation [Dorigo & Di Caro, 1999; Dorigo et al, 2006; Stützle & Hoos, 2000] is an optimisation technique, which takes inspiration from the way in which colonies of ants communicate with each other by depositing pheromones, and find favourable paths towards food sources. The inspiration for Ant Colony Optimisation came from experiments performed by Goss, Aron, Deneubourg, & Pasteels (1989), and Deneubourg, Aron, Goss & Pasteels (1990) involving a colony of ants and a food source, with the path from the nest to the food source being a choice of two bridges. In the experiment by Goss et al (1989), the two bridges are different lengths and after time, the ants eventually choose the shorter one; in the experiment by Deneubourg et al (1990), the bridges are the same length, and due to random fluctuations, the ants eventually began to favour

Chapter 2 Literature Review

18 one bridge, although after repeating the experiment several times, it was found that each bridge was favoured about 50% of the time. Optimisation techniques based on the behaviour of ants were then proposed in the early 1990s [Dorigo, Maniezzo & Colorni, 1991; Dorigo, 1992], and the Ant Colony Optimisation technique developed from there.

Ant Colony Optimisation is an iterative process, and at each stage, every ant deposits a certain amount of pheromones to indicate the quality of the path it has taken. The ants can sense nearby pheromones and are naturally drawn towards areas where the pheromone concentration is high [Dorigo et al, 2006]. Over time, pheromones evaporate if none are deposited for any length of time. Therefore, when a favourable path is chosen, the pheromone strength increases, which increases the probability of other ants choosing that path, which increases the pheromone strength again until eventually, all the ants follow that path.

The basic theory of Ant Colony Optimisation, as well as a summary of different variations and applications can be found in Dorigo et al (2006), which also gives an example of the application of Ant Colony Optimisation to the Travelling Salesman Problem. In fact, the Travelling Salesman Problem is a very common application of Ant Colony Optimisation, and many papers have been published on this [Dorigo & Gambardella, 1997; Dorigo et al, 2006; Dorigo, Maniezzo & Colorni 1996; Stützle & Hoos, 2000]. Other successful applications of Ant Colony Optimisation include routing problems in telecommunication networks [Schoonderwoerd, Holland, Bruten & Rothkrantz, 1996], project scheduling [Merkle, Middendorf & Schmeck, 2002], and finding optimal solutions for robotic path planning problems [Ma, Duan & Liu, 2007; Zhang, Wu, Peng & Jiang, 2009]. The paper by Parunak, Purcell & O’Connell (2002) applies a digital pheromone concept to the coordination of swarming UAVs, with each specific location in the search space having a particular pheromone level, rather than having pheromones along the edges that connect different points. The results from this paper indicate that this technique is suitable for coordinating UAV swarms. This version is similar to that tested in this thesis.

Another nature-inspired algorithm that is becoming increasingly popular is Bacterial Foraging Optimisation [Das, Biswas, Dasgupta & Abraham, 2009; Niu, Fan, Tan, Rao & Li, 2010; Passino, 2002; Liu & Passino, 2002]. This algorithm is based on the foraging behaviour of bacteria such as E.coli as they search for nutrients [Passino, 2002]. Based on this foraging behaviour, the Bacterial Foraging Optimisation algorithm was proposed in 2002 by Passino, and has become increasingly popular over the last decade. However, this algorithm has not been applied to the simulations in this thesis, as it is felt that it is not suitable for the particular simulations being carried out. There are two main reasons for this: firstly, based on the algorithm description in Passino (2002), each bacterium (and hence, each agent in this case) takes turns to evaluate several solutions. This is practical if solutions can be evaluated instantly (or very quickly) but in this case, it would be impractical because it takes time for the agents to travel to their “solutions” and hence, a lot of time

Chapter 2 Literature Review

19 would be wasted. The second reason is that given the time constraints imposed by fuel consumption, the algorithm would not get a chance to develop and as a result, the algorithm would behave in a similar way to the Hill Climbing algorithm, as the start of the algorithm is similar to this method. Therefore, Bacterial Foraging Optimisation has not been applied to the simulations.

2.5 Summary

This chapter provided a review of some of the literature associated with the main topics discussed in this thesis. The main topics discussed are control methodologies, the development of USAR operations, the standard patterns for maritime search and rescue operations, and optimisation techniques. The main focus of this thesis is the application of optimisation techniques to an autonomous system for air-sea rescue, with the emphasis being on the coordination of a group of agents to detect targets.

Although various control methodologies have been used for helicopter control, two specific control methodologies were discussed: PID Control and Sliding Mode Control. A brief background of PID Control was presented, as was key literature describing the theory and some applications of this technique. Methods of tuning the gains were also discussed, and the relevant literature was given. Sliding Mode Control was then discussed in terms of origin, theory and applications, along with the relevant literature.

Next, an overview of the development of autonomous USAR was presented, as well as the challenges associated with this. Then, three standard search patterns for maritime rescue were discussed: Parallel Sweep, Sector Search, and Expanding Square. A brief description of the theory and effectiveness of each technique was given, as was the main source of information about them. Finally, several optimisation techniques were introduced, along with relevant literature. First of all, two basic algorithms were discussed: Random Search, and Hill Climbing. Next, Simulated Annealing was presented, along with the literature that describes the inspiration for this technique and the development of it. Then, three biologically-inspired techniques were discussed: Genetic Algorithms, Particle Swarm Optimisation, and Ant Colony Optimisation, which are designed to mimic natural processes. i.e. evolution, the flocking of birds, and path coordination in ants. The literature describing the original development of these algorithms was presented, as well as a basic account of the theory of each of these techniques. Several applications of each technique were presented, including applications relevant to the work being carried out in this thesis. A brief description of the theory and development of another popular biologically-inspired algorithm was then presented: Bacterial Foraging Optimisation. However, as explained, this method is not used in this thesis because it would not get a chance to develop properly and it would behave in a similar way to Hill Climbing.

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Chapter 3