This paper deals with a new hardware/software embedded system design methodology based on design pattern approach by development of a new design tool called smartcell. Three main constraints of embedded systems design process are investigated: the complexity, the partitioning between hardware and software aspects and the reusability. Two intermediate models are carried out in order to solve the...

In this paper, we study the container stacking problem (CSP) which is one of the most important problems in marine terminal. An optimisation model is developed in order to determine the optimal storage strategy for various container-handling schedules. The objective of the model is to minimise the distance between vessel berthing location and the storage positions of containers. The CSP is solv...

Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colo...

Data Grid replication is critical for improving data intensive applications performance, providing fault tolerance and load balancing. Most of the techniques for data replication use Giggle as a framework for Replica Location Services (RLS), combined with other services for replica selection and optimization. Our previous work have proposed an enhanced Giggle framework, that simplify the locati...

In this paper, an efficient multi-objective model is proposed to solve time-cost trade off problem considering cash flows. The proposed multi-objective meta-heuristic is based on Ant colony optimization and is called Non Dominated Archiving Ant Colony Optimization (NAACO). The significant feature of this work is consideration of uncertainties in time, cost and more importantly interest rate. A ...

This paper investigates the effect of the cost matrix standard deviation of Travelling Salesman Problem (TSP) instances on the performance of a class of combinatorial optimisation heuristics. Ant Colony Optimisation (ACO) is the class of heuristic investigated. Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in an...

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for real-world problems we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary ...

In our previous work we have proposed a hybrid Particle Swarm Optimisation / Ant Colony Optimisation (PSO/ACO) algorithm for discovering classification rules. In this paper we propose some modifications to the algorithm and apply it to a challenging hierarchical classification problem. This is a bioinformatics problem involving the prediction of G-ProteinCoupled Receptor’s (GPCR) hierarchical f...

Ant Colony Optimisation (ACO) algorithms are inspired by the foraging behaviour of real ants and are a relatively new class of algorithm which have shown promise when applied to combinatorial optimisation problems. In recent years ACO algorithms have begun to gain popularity and as such are beginning to be applied to more complex problem domains including (but not limited to) dynamic problems. ...

Stochastic local search (SLS) methods like evolutionary algorithms, ant colony optimisation or iterated local search receive an ever increasing attention for the solution of highly application relevant optimisation problems. Despite their noteworthy successes, several issues still hinder their even wider spread. One central issue is the configuration and parameterisation of SLS methods, which i...