Continuous function optimization using hybrid ant colony approach with orthogonal design scheme

A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. The performance and a comparison with CACO and FEP have been studied

Orthogonal methods based ant colony search for solving continuous optimization problems

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

Today\u27s signature-based intrusion detection systems are reactive in nature and storage-limited. Their operation depends upon catching an instance of an intrusion or virus and encoding it into a signature that is stored in its anomaly database, providing a window of vulnerability to computer systems during this time. Further, the maximum size of an Internet Protocol-based message requires the database to be huge in order to maintain possible signature combinations. In order to tighten this response cycle within storage constraints, this paper presents an innovative Artificial Immune System-inspired Multiobjective Evolutionary Algorithm. This distributed intrusion detection system (IDS) is intended to measure the vector of tradeoff solutions among detectors with regard to two independent objectives: best classification fitness and optimal hypervolume size. Our antibody detectors promiscuously monitor network traffic for exact and variant abnormal system events based on only the detector\u27s own data structure and the application domain truth set, responding heuristically. Applied to the MIT-DARPA 1999 insider intrusion detection data set, our software engineered algorithm correctly classifies normal and abnormal events at a high level which is directly attributed to a detector affinity threshold

Contribution to the Control of a MAS's Global Behaviour: Reinforcement Learning Tools

Reactive multi-agent systems present global behaviours uneasily linked to their local dynamics. When it comes to controlling such a system, usual analytical tools are difficult to use so specific techniques have to be engineered. We propose an experimental dynamical approach to enhance the control of the global behaviour of a reactive multi-agent system. We use reinforcement learning tools to link global information of the system to control actions. We propose to use the behaviour of the system as this global information. The behaviour of the whole system is controlled thanks to actions at different levels instead of building the behaviours of the agents, so that the complexity of the approach does not directly depend on the number of agents. The controllability is evaluated in terms of rate of convergence towards a target behaviour. We compare the results obtained on a toy example with the usual approach of parameter setting

Hybrid Continuous Interacting Ant Colony aimed at enhanced global optimization

Ant colony algorithms are a class of metaheuristics which are inspired from the behaviour of real ants. The original idea consisted in simulating the stigmergic communication, therefore these algorithms are considered as a form of adaptive memory programming. A new formalization was proposed for the design of ant colony algorithms, introducing the biological notions of heterarchy and communication channels. We are interested in the way ant colonies handle the information. According to these issues, a heterarchical algorithm called “Continuous Interacting Ant Colony ” (CIAC) was previously designed for the optimization of multiminima continuous functions. We propose in that paper an improvement of CIAC, by the way of a hybridization with the local search Nelder-Mead algorithm. The new algorithm called “Hybrid Continuous Interacting Ant Colony ” (HCIAC) compares favorably with some competing algorithms on a large set of standard test functions

Métaheuristiques d'optimisation vues sous l'angle de l'échantillonnage de distribution

International audienceno abstrac

Continuous interacting ant colony algorithm based on dense heterarchy

Ant colony algorithms are a class of metaheuristics which are inspired from the behavior of real ants. The original idea consisted in simulating the stigmergic communication, therefore these algorithms are considered as a form of adaptive memory programming. A new formalization is proposed for the design of ant colony algorithms, introducing the biological notions of heterarchy and communication channels. We are interested in the way ant colonies handle the information. According to these issues, a heterarchical algorithm called “Continuous Interacting Ant Colony ” (CIAC) is designed for the optimization of multiminima continuous functions. CIAC uses two communication channels showing the properties of trail and direct communications. CIAC presents interesting emergent properties as it was shown through some analytical test functions

Metaheuristics for continuous variables. The registration of retinal angiogram images

International audienceno abstrac

Artificial ants for continuous optimization

International audienceno abstrac