Fuzzy logic has been a useful tool for modeling complex problems, with the use of fuzzy variables and fuzzy rules, and in this paper we use a fuzzy system for parameter adaptation in the Ant Colony Optimization (ACO metaheuristic). In this case we perform the dynamic adaptation of Alpha and Rho parameters; this is to control the abilities of ACO to perform a global and local search. Simulation ...

In this paper the analogy between biological swarms and artificial multiagent systems is pointed out. As an example the steps required to model the artificial optimization technique called Ant Colony Optimization starting from the foraging behaviour of natural ant colonies are explained in detail. During the development of the model the authors use the language of multiagent systems to highligh...

The aim of the paper is to investigate methods for deriving a suitable set of test paths for a software system. The design and the possible uses of the software system are modelled by a Markov Usage Model which reflects the operational distribution of the software system and is enriched by estimates of failure probabilities, losses in case of failure and testing costs. Exploiting this informati...

A system based on the adaptation of the search principle used in ant colony optimization (ACO) for multiobjective graph-based data mining (GBDM) is introduced in this paper. Our multiobjective ACO algorithm is designed to retrieve the best substructures in a graph database by jointly considering two criteria, support and complexity. The experimental comparison performed with a classical GBDM me...

The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elemen...

A multi-agent approach to solving dynamic Traveling Salesman Problem (TSP) is presented. In the dynamic version of TSP cities can be dynamically added or removed. Proposed multi-agent approach is based on the Sensitive Stigmergic Agent System model refined with new type of messages between agents. The agents send messages every time a change occurs, for instance, when an agent observes that a c...

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.

Two novel extensions for the well known Ant Colony Optimization (ACO) framework are introduced here, which allow the solution of Mixed Integer Nonlinear Programs (MINLP). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results. These extensions on the ACO framework have bee...

In reputation systems, how to obtain node’s reputation is a crucial issue. To solve this problem, a two-layer reputation search mechanism based on ant colony and grouping is proposed. The first layer is the monitoring platform. According to physical topology, it divides nodes into groups. Each group uses network segment as its identification, and it has a super node and a candidate node. The se...

Using ant colony algorithm to solve TSP (traveling salesman problem) has some disadvantages as easily plunging into local minimum, slow convergence speed and so on. In order to find the optimal path accurately and rapidly, an improved ant colony algorithm is proposed. Experimental results show that the improved ant colony algorithm has better effectiveness for TSP problems solutions.