Inspired by ant colony optimization algorithm, a new firefly optimization algorithm is presented which we call firefly colony optimization algorithm (FCO). Unlike the standard firefly algorithm, the proposed approach is a distributed, and constructive greedy metaheuristic which uses the positive feedback to construct greedily good solutions, and to avoid a premature convergence to low quality s...

Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. Originally the method to construct decision tree follows greedy approach which results in local tree and classification rules. Ant Colony Optimization (ACO) is a metaheuristic approach used to get more optimal solution compared to other methods from large search space. In propo...

Dealing with ill-defined problems, where the actual values of input parameters are unknown or not directly measurable, is generally not an easy task. In this paper, we propose a hybrid metaheuristic approach, incorporating a sampling-based simulation module, in order to enhance the robustness of the final solutions. Empirical application to the classical mean-variance portfolio optimization pro...

In this paper we propose three metaheuristic approaches, namely a Tabu Search, an Evolutionary Computation and an Ant Colony Optimization approach, for the edge-weighted k-cardinality tree (KCT) problem. This problem is an NP-hard combinatorial optimization problem that generalizes the well-known minimum weight spanning tree problem. Given an edge-weighted graph G = (V; E), it consists of /ndin...

When deploying a new mobile technology such as LTE, it is crucial to identify the factors that affect the radio network in terms of capacity and quality of service. In this context, network coverage is arguably the single most influential factor. This work presents a metaheuristic-optimization approach that automatically adapts the parameters of a signal-propagation model. The optimization proc...

We present a metaheuristic optimization framework based on a Collaborative Agent Teams (CAT) architecture to tackle large-scale mixed-integer optimization problems with complex structures. This framework introduces several conceptual improvements over previous agent teams approaches. We discuss how to configure the three key components of a CAT solver for a particular multidimensional optimizat...