Solving complex problems with a bioinspired model

Membrane systems are parallel and bioinspired systems which simulate membranes behavior when processing information. As a part of unconventional computing, P-systems are proven to be effective in solvingcomplexproblems. A software technique is presented here that obtain good results when dealing with such problems. The rules application phase is studied and updated accordingly to obtain the desired results. Certain rules are candidate to be eliminated which can make the model improving in terms of time

Designing Conducting Polymers Using Bioinspired Ant Algorithms

Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solution depositing virtual pheromone proportional to how good a specific solution is. This creates a autocatalytic (positive feedback) process that can be used to generate automatic solutions to very difficult problems. In the present work we show that these algorithms can be used coupled to tight-binding hamiltonians to design conducting polymers with pre-specified properties. The methodology is completely general and can be used for a large number of optimization problems in materials science

Macromodelling for analog design and robustness boosting in bio-inspired computing models

Setting specifications for the electronic implementation of biological neural-network-like vision systems on-chip is not straightforward, neither it is to simulate the resulting circuit. The structure of these systems leads to a netlist of more than 100.000 nodes for a small array of 100×150 pixels. Moreover, introducing an optical input in the low level simulation is nowadays not feasible with standard electrical simulation environments. Given that, to accomplish the task of integrating those systems in silicon to build compact, low power consuming, and reliable systems, a previous step in the standard analog electronic design flux should be introduced. Here a methodology to make the translation from the biological model to circuit-level specifications for electronic design is proposed. The purpose is to include non ideal effects as mismatching, noise, leakages, supply degradation, feedthrough, and temperature of operation in a high level description of the implementation, in order to accomplish behavioural simulations that require less computational effort and resources. A particular case study is presented, the analog electronic implementation of the locust's Lobula Giant Movement Detector (LGMD), a neural structure that fires a collision alarm based on visual information. The final goal is a collision threat detection vision system on-chip for automotive applications.European Union IST-2001-38097, TIC2003 - 09817-C02-0

Modeling of Biological Intelligence for SCM System Optimization

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

ZASTOSOWANIE ALGORTYMU SELEKCJI KLONALNEJ DO SYNTEZY REGULATORA PID SYSTEMÓW MIMO W PRZEMYŚLE PETROCHEMICZNYM

This paper presents the results of the Smart technologies application to the synthesis of MIMO-systems in oil and gas industry. In particular, there is considered a multidimensional multiply connected system for gas distillation process control through a distillation column with regulators configured on the basis of Smart-technologies – clonal selection algorithm (CLONALG) of an artificial immune system (AIS).W artykule przedstawiono wyniki zastosowania inteligentnych technologii do syntezy systemów MIMO w przemyśle petrochemicznym. W szczególności rozważany jest wielowymiarowy układ sterowania procesem destylacji gazu w kolumnie destylacyjnej z regulatorami skonfigurowanymi na podstawie tzw. inteligentnych technologii – algorytmu doboru klonów (CLONALG) sztucznego układu immunolgicznego (AIS)

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

[EN] Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints