The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers [1]. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant’s behaviors. Since the first ant system algorithm was proposed, there is...

We propose an algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for solving the Multidimensional Knapsack Problem (MKP), the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. The proposed ACO algorithm is generic, and we propose three different instantiations, corresponding to three different ways...

In an object-based distributed computing system, the use of location-independent naming scheme can improve the system’s transparency, scalability, and reliability. Names need to be resolved prior to passing messages between objects. This paper presents an Adaptive RandoMised Structured search network termed ARMS that provides name resolution by forwarding a query through neighbouring nodes. ARM...

Ant colony algorithm, a heuristic simulated algorithm, provides better solutions for non-convex, non-linear and discontinuous optimization problems. For ant colony algorithm, it is frequently to be trapped into local optimum, which might lead to stagnation. This article presents the city-select strategy, local pheromone update strategy, optimum solution prediction strategy and local optimizatio...

In this paper, we present a dynamic ant colony optimisation (ACO) algorithm to solve dynamic traffic routing problem. The main objective of this work is to search out the least-time-cost route in a variable-edge-weight graph. We introduce time-dependent pheromones and electric-field model as two heuristic factors to improve the basic ACO. The simulation results show that the proposed dynamic AC...

In this paper a search for the logical variants of gene-gene interactions in genome-wide association study (GWAS) data using ant colony optimisation is proposed. The method based on stochastic algorithms is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover logical operations between combinations of single nucleotide polymorphisms tha...

Abs t rac t . In this paper we introduce an Ant Colony Optimisation (ACO) algorithm for the Shortest Common Supersequence (SCS) problem, which has applications in production system planning, mechanical engineering and molecular biology. The ACO algorithm is used to find good parameters for a heuristic for the SCS problem. An island model with several populations of ants is used for the ACO algo...

Iterative rule learning is a common strategy for fuzzy rule induction using stochastic population-based algorithms (SPBAs) such as Ant Colony Optimisation and genetic algorithms. Several SPBAs are run in succession with the result of each being a rule added to an emerging final ruleset. Between SPBA runs, cases in the training set that are covered by the newly evolved rule are generally removed...

The n aircraft conflict resolution problem is highly combinatorial and can be optimally solved using classical mathematical optimisation techniques only for small problems involving less than 5 aircraft. This article applies an Ant Colony Optimization (ACO) algorithm in order to solve large problems involving up to 30 aircraft. In order to limit the number of pheromone trails to update, a n air...

In this work we present two new multiobjective proposals based on ant colony optimisation and random greedy search algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. Some variants of these algorithms have been compared in order to find out the impact of different design configurations and the use of heuristic information. Go...