An adaptive parallel ant colony optimisation (PACO) algorithm on massively parallel processors (MPPs) is presented. In the algorithm, we propose a strategy for information exchange between processors that makes each processor choose a partner to communicate with and update their pheromone adaptively. We also propose a method of adaptively adjusting the time interval for the exchange of informat...

Nature-inspired algorithms such as genetic algorithms, particle swarm optimisation and ant colony algorithms have successfully solved computer science problems of search and optimisation. The initial implementations of these techniques focused on static problems solved on single machines. These have been extended by adding parallelisation capabilities in the vein of distributed computing with a...

Many bio-inspired algorithms (evolutionary algorithms, artificial immune systems, particle swarm optimisation, ant colony optimisation, ...) are based on populations of agents. Stepney et al [2005] argue for the use of conceptual frameworks and meta-frameworks to capture the principles and commonalities underlying these, and other bio-inspired algorithms. Here we outline a generic framework tha...

Ant colony optimization and artificial potential field were used respectively as global path planning and local path planning methods in this paper. Some modifications were made to accommodate ant colony optimization to path planning. Pheromone generated by ant colony optimization was also utilized to prevent artificial potential field from getting local minimum. Simulation results showed that ...

Dynamic problems and nature inspired meta-heuristics. Originally published in Studies in computational intelligence: Biologically-inspired optimisation methods: parallel algorithms, systems and applications, Chapter 4, pp. 79-110 Summary. Biological systems have often been used as the inspiration for search techniques to solve continuous and discrete combinatorial optimisation problems. One of ...

This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region...

This paper describes GRAF-ANT (Graphical Framework for Ant Colony Optimization), an objectoriented C# framework for developing ant colony systems that we have developed. While developing this framework, abstractions that are necessary for ant colony optimization algorithms were analyzed, as well as the features that their implementing classes should have. During creation of these classes, sever...

Ants often form mutualistic interactions with aphids, soliciting honeydew in return for protective services. Under certain circumstances, however, ants will prey upon aphids. In addition, in the presence of ants aphids may increase the quantity or quality of honeydew produced, which is costly. Through these mechanisms, ant attendance can reduce aphid colony growth rates. However, it is unknown ...