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Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning...
The paper deals with a modification in the learning phase of AntNet routing algorithm, which improves the system adaptability in the presence of undesirable events. Unlike most of the ACO algorithms which consider reward-inaction reinforcement learning, the proposed strategy considers both reward and penalty onto the action probabilities. As simulation results show, considering penalty in AntNet routing...
This paper proposes the design of fuzzy-rule-based systems using continuous ant-colony optimization (RCACO). RCACO determines the number of fuzzy rules and optimizes all the free parameters in each fuzzy rule. It uses an online-rule-generation method to determine the number of rules and identify suitable initial parameters for the rules and then optimizes all the free parameters using continuous ant-colony...
This paper aims to be a short literature review, presenting the top five most promising algorithms for scheduling, as identified by us from the technical and scientific literature of the past years: Task Swap, Squeaky Wheel Optimization, Value-Biased Stochastic Search, Bee Colony Optimization And Temporal Difference (lambda), from reinforcement learning. We wanted to cover permutation-state methods,...
Ant colony optimization (ACO) is a kind of bionic swarm intelligence algorithm belongs to artificial intelligence (AI) field and has been successfully applied in resolving complex optimization problems. Support vector machine (SVM) is a new machine learning method with greater generalization performance, and has shown its superiority in classification and regression problems. By combining the advantages...
In this paper, we propose a swarm intelligence based reinforcement learning (SWIRL) method to train artificial neural networks (ANN). Basically, two swarm intelligence based algorithms are combined together to train the ANN models. Ant Colony Optimization (ACO) is applied to select ANN topology, while Particle Swarm Optimization (PSO) is applied to adjust ANN connection weights. To evaluate the performance...
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