Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Optimatization of hybrid renewable energy systems on isolated microgrids : a smart grid approach

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2016The energy systems of small isolated communities face great challenges related to their autonomy and resilience, when looking for a sustainable energy future. Hybrid renewable energy systems, composed from different technologies, partially or totally renewable, potentiates a growing security of supply for these isolated micro-communities. Moreover, with a smart grid approach, the possibility to reschedule part of the electricity load is seen as a promising opportunity to delay further investments on the grid’s power capacity, enabling a better grid management, through peak load control, but also to promote a more efficient use of endogenous resources, maximizing renewable penetration. To identify the micro-communities main energy challenges, a literature review was taken, reporting the design and implementation of isolated hybrid renewable energy systems. Since electricity and heat energy vectors can be, in part, assured by endogenous resources, a methodology to optimize demand response on isolated hybrid renewable energy systems was developed, using the electric backup of solar thermal systems for domestic hot water supply as flexible loads. This approach is intended to increase energy efficiency of the energy system, reducing grid operation costs and associated CO2 emissions. A model of the electric impact of the implementation of solar thermal systems and heat pumps for domestic hot water supply was developed and tested for the Corvo Island case study, a small and isolated microgrid, located in the mid-Atlantic with around 400 inhabitants and a diesel power plant. An impact of 60% on peak load and 7% on annual electricity demand was found. In order to tackle this significant impact in the grid, a model for optimizing the economic dispatch of the island was developed, testing multiple demand response approaches to the backup loads, from heuristics to genetic algorithms, having this last one performed best to control the peak load and minimize the operation costs. Nonetheless, there was the need to compare and validate the demand response optimization strategies of this developed model with other available modeling tools, which in the end presented similar results. As the pillar of this thesis is the optimization of hybrid renewable energy systems, the influence of the uncertainties associated to renewables forecast had to be studied, in particular its impact on the demand response scheduling. Wind uncertainties demonstrated to have a greater impact on the grid than the solar ones. Finally, the methodology developed incrementally along the thesis and validated in Corvo Island, was tested on different scales and types of isolated systems. It demonstrated to be especially suitable for small systems with less than 20 MW power installed and over 25% renewable generation, with mostly residential load profiles

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Multi-agent evolutionary systems for the generation of complex virtual worlds

Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer’s intent through interaction, and encourages playful discovery

The integration of pumped hydro storage systems into PV microgrids in rural areas

Photovoltaic (PV) systems are popular in rural areas because they provide low cost and clean electricity for homes and irrigation systems. The primary challenge of PV systems is their intermittent nature. The typical solution is storing energy in batteries; however, they are expensive and possess a short lifespan. This research proposes a new type of pumped hydro storage (PHS) which can be implemented as an alternative to batteries. The components of the system are modelled to consider losses of the system accurately. The mathematic model developed in this project assists the management system to make more efficient decisions. The proposed storage is integrated into a farmhouse that has a PV pumping system where economic aspects of implementing the proposed storage is investigated. The integration of the proposed PHS into a microgrid needs a management system to make this system efficient and 3 cost-effective. This research proposes a multi-stage management system to schedule and control the microgrid components for optimal integration of the PHS. The designed management system is able to manage the pump, turbine, and irrigation time on real-time taking into account both present and future conditions of the microgrid. This study investigates the technical aspects of the proposed system. The PHS and the management system are tested experimentally in a setup installed at smart energy laboratory at Edith Cowan university. Data used in this project are real data collected in the laboratory in order to have a realistic analysis. Economic analysis is done in different sizes with different conditions. Results indicate that the proposed system has a short payback period and a large lifetime benefit, featuring as a cost-effective and sustainable energy storage system for use in rural areas. Video abstract: https://youtu.be/VuyEvHRY7W

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques