We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters : pheromone trails are associated with parameter values ; these pheromone trails represent the learnt desirability of using parameter values and are used to dynamically set parameters in a probabilistic way. The two fra...

In this paper a new generation ACO-Based Planner, called ACOPlan 2013, is described. This planner is an enhanced version of ACOPlan, a previous ACO-Based Planner (Baioletti et al. 2011), which differs from the former in the search algorithm and in the implementation, now done on top of Downwards. The experimental results, even if are not impressive, are encouraging and confirm that ACO is a sui...

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization 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 recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, e...

Feature Selection is the process of selecting a subset of features available, allowing a certain objective function to be optimized, from the data containing noisy,irrelevant and redundant features. This paper presents a novel feature selection method that is based on hybridization of ACO with a binary PSO to obtain excellent properties of two algorithms by synthesizing them and aims at achievi...

We present a model for the travelling salesman problem (TSP) solved using the ant colony optimisation (ACO), a bio-inspired mechanism that helps speed up the search for a solution and that can be applied to many other problems. The natural complexity of the TSP combined with the selforganisation and emergent behaviours that result from the application of the ACO make model-checking this system ...

Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper first proposes an adaptive strategy for the volatility rate of pheromone trail according to ...

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 ...

Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can ev...

Decision trees have been widely used in data mining andmachine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combini...