An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario

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Busca de informação distribuída usando heurísticas adaptativas para agentes móveis em tempo real

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2010Com a contínua expansão da Internet, a busca de informação relevante deve atender requisitos de tempo e qualidade. A área da inteligência artificial possui soluções de busca informada sofisticadas que tratam essas variáveis: tempo e qualidade. Esses sistemas de busca de informação são sistemas distribuídos de tempo real, uma área emergente de pesquisa e que tem nos apresentado soluções de arquitetura computacional com alta escalabilidade. Uma alternativa para a busca de informação em tempo real é a tecnologia de agentes móveis, a qual vem sendo objeto de pesquisa desde antes da popularização da Internet, mas vem ganhando força com a padronização desta tecnologia através da FIPA, uma fundação filiada ao IEEE. A introdução de agentes móveis no cenário de sistemas distribuídos traz diversas vantagens e possuem características que são altamente desejáveis para sistemas distribuídos de tempo real. Os agentes móveis também utilizam técnicas originadas na inteligência artificial para melhorar seu desempenho em cenários com restrição temporal. Uma dessas técnicas é a de anytime algorithms, que permite ao algoritmo controlar a qualidade da resposta em função do tempo de execução. Neste trabalho serão apresentados cenários envolvendo busca de informação em servidores distribuídos. Para realizar experimentos nestes cenários, serão apresentadas novas heurísticas para determinação de itinerário de agentes móveis na busca de informação em ambientes distribuídos com características de tempo real. Serão utilizadas plataformas de agentes móveis existentes no mercado, padrões de tecnologia de agentes e também técnicas de controle de execução de algoritmos em ambientes de tempo real

Multi-guide Particle Swarm Optimisation for Dynamic Multi-objective Optimisation Problems

This study investigates the suitability of, and adapts, the multi-guide particle swarm optimisation (MGPSO) algorithm for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach, originally developed for static multi-objective optimisation problems (SMOPs), where each subswarm optimises one of the objectives. It uses a bounded archive that is based on a crowding distance archive implementation. Compared to static optimization problems, DMOPs pose a challenge for meta-heuristics because there is more than one objective to optimise, and the location of the Pareto-optimal set (POS) and the Pareto-optimal front (POF) can change over time. To efficiently track the changing POF in DMOPs using MGPSO, six archive management update approaches, eight archive balance coefficient initialization strategies, and six quantum particle swarm optimisation (QPSO) variants are proposed. To evaluate the adapted MGPSO for DMOPs, a total of twenty-nine well-known benchmark functions and six performance measures were implemented. Three experiments were run against five different environment types with varying temporal and spatial severities. The best strategies from each experiment were then compared with the other dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that the adapted MGPSO achieves very competitive, and often better, performance compared to existing DMOAs

VANET-enabled eco-friendly road characteristics-aware routing for vehicular traffic

There is growing awareness of the dangers of climate change caused by greenhouse gases. In the coming decades this could result in numerous disasters such as heat-waves, flooding and crop failures. A major contributor to the total amount of greenhouse gas emissions is the transport sector, particularly private vehicles. Traffic congestion involving private vehicles also causes a lot of wasted time and stress to commuters. At the same time new wireless technologies such as Vehicular Ad-Hoc Networks (VANETs) are being developed which could allow vehicles to communicate with each other. These could enable a number of innovative schemes to reduce traffic congestion and greenhouse gas emissions. 1) EcoTrec is a VANET-based system which allows vehicles to exchange messages regarding traffic congestion and road conditions, such as roughness and gradient. Each vehicle uses the messages it has received to build a model of nearby roads and the traffic on them. The EcoTrec Algorithm then recommends the most fuel efficient route for the vehicles to follow. 2) Time-Ants is a swarm based algorithm that considers not only the amount of cars in the spatial domain but also the amoumt in the time domain. This allows the system to build a model of the traffic congestion throughout the day. As traffic patterns are broadly similar for weekdays this gives us a good idea of what traffic will be like allowing us to route the vehicles more efficiently using the Time-Ants Algorithm. 3) Electric Vehicle enhanced Dedicated Bus Lanes (E-DBL) proposes allowing electric vehicles onto the bus lanes. Such an approach could allow a reduction in traffic congestion on the regular lanes without greatly impeding the buses. It would also encourage uptake of electric vehicles. 4) A comprehensive survey of issues associated with communication centred traffic management systems was carried out