We consider the problem of learning heuristics for controlling forward state-space search in AI planning domain. We draw on a recent framework for “structured output classification” (e.g. syntactic parsing) known as learning as search optimization (LaSO). The LaSO approach uses discriminative learning to optimize heuristic functions for search-based computation of structured outputs and has sho...

Ant colony optimization (ACO) algorithms have originally been designed for static problems, where the input data is known in advance and not subject to changes over time. Later, long term memory of ACO proved effective reoptimization environment when extended deal with dynamic combinatorial problems (DCOPs). Among major proposals this kind, several adaptations procedures improve information reu...

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspire...

This paper studies the effect of fixing the length of the selected feature subsets on the performance of ant colony optimization (ACO) for feature selection (FS) for supervised learning. It addresses this concern by investigating: (1) determining the optimal feature subset from datamining perspective, (2) demonstrating the solution convergence in case of fixing the length of the selected featur...

This article proposes to solve the oversaturated network traffic signal coordination problem using the Ant Colony Optimization (ACO) algorithm. The traffic networks used are discrete time models which use green times at all the intersections throughout the considered period of oversaturation as the decision variables. The ACO algorithm finds intelligent timing plans which take care of dissipati...

Developing models that can represent biochemical systems is one of the hallmarks of systems biology. Scientists have been gathering data from actual experiments, but there is a lack in computer models that can be used by scientists in analysing the various biochemical systems more effectively. In this research, we propose to use an ant colony optimization (ACO) algorithm for the network inferen...

Time constraint is the main factor in real time operating system. Different scheduling algorithm is used to schedule the task. The Earliest Deadline First and Ant Colony Optimization is a dynamic scheduling algorithm used in a real time system and it is most beneficial scheduling algorithm for single processor real-time operating systems when the systems are preemptive and under loaded. The mai...

In this paper an ant colony optimization (ACO) algorithm for the minimum connected dominating set problem (MCDSP) is presented. The MCDSP become increasingly important in recent years due to its applicability to the mobile ad hoc networks (MANETs) and sensor grids. We have implemented a one-step ACO algorithm based on a known simple greedy algorithm that has a significant drawback of being easi...

Ant Colony optimization has proved suitable to solve a wide range of combinatorial optimization (or NP-hard) problems as the Travelling Salesman Problem (TSP). The first step of ACO algorithm is to set the parameters that drive the algorithm. The parameter has an important impact on the performance of the ant colony algorithm. The basic parameters that are used in ACO algorithms are; the relati...

For solving large instances of the Travelling Salesman Problem (TSP), the use of a candidate set (or candidate list) is essential to limit the search space and reduce the overall execution time when using heuristic search methods such as Ant Colony Optimisation (ACO). Recent contributions have implemented ACO in parallel on the Graphics Processing Unit (GPU) using NVIDIA CUDA but struggle to ma...