Mutual Attraction Guided Search: a novel solution method to the Traveling Salesman Problem with vehicle dynamics

Traveling Salesman Problem (TSP) solution techniques are often used for route planning for automated vehicles. Most TSP solution methods focus on path length as the fitness reference, however in many cases, traversal time is of more practical importance. Mutual Attraction Guided Search (MAGS) is a novel solution method that uses an iterative process to simultaneously optimize both angle of travel through each target as well as the ordering of the targets in order to optimize path traversal time. MAGS deterministically locates a locally optimum solution quickly and can optimize for the acceleration limits of a specific vehicle rather than requiring a constant vehicle speed. Since the basic form of MAGS finds a solution deterministically, it has no mechanism for escaping local minima, therefore an evolutionary form is also developed that alternates between local search with MAGS and global search using evolutionary operators to combine and mutate solutions. This hybridization provides the necessary balance between local and global search that is required to locate a globally optimal solution. A fitness based on approximate travel time based on the maximum velocity achievable at each point on the path is calculated using the curvature of the path and the dynamic constraints of the vehicle. The performance of both the basic and evolutionary forms of MAGS are compared against path length based Euclidean and curvature constrained TSP methods --Abstract, page iii

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Environmental Impact Assessment by Green Processes

Primary energy consumption around the world has been increasing steadily since the Industrial Revolution and shows no signals of slowing down in the coming years. This trend is accompanied by the increasing pollutant concentration on the Earth’s biosystems and the general concerns over the health and environmental impacts that will ensue. Air quality, water purity, atmospheric CO2 concentration, etc., are some examples of environmental parameters that are degrading due to human activities. These ecosystems can be safeguarded without renouncing industrial development, urban and economic development through the use of low environmental impact technologies instead of equivalent pollutant ones or through the use of technologies to mitigate the negative impact of high emissions technologies. Pollutant abatement systems, carbon capture technologies, biobased products, etc. need to be established in order to make environmental parameters more and more similar to the pre-industrialization values of the planet Earth. In 15 papers international scientists addressed such topics, especially combining a high academic standard coupled with a practical focus on green processes and a quantitative approach to environmental impacts

No Coward Plays Hockey

This thesis examined the landscape of women’s hockey in Canada, and focused on the national women’s hockey team, and how the treatment of female hockey players in the Canadian media, and in the eyes of the Canadian public, differs from the treatment of male hockey players. This thesis drew on three different research methods: an ethical/philosophical analysis, a media analysis and a narrative analysis. The ethical analysis took a philosophical approach and discussed the different rules in men’s and women’s hockey. The ethical analysis also discussed other issues in hockey such as paternalism versus free will, and gender segregation in sport. The media analysis consisted of a content analysis centering on major Canadian newspapers published over the last 29 years, in order to see how these newspapers viewed female hockey players and women’s hockey in general. Finally, this thesis included a narrative analysis. The narrative analysis consisted of two separate types of narratives: a story analyst approach; and a personal narrative approach. The story analyst approach acted as a continuation of the media analysis and examined key themes and ideas from the media analysis and created a story from those data. The personal experience narrative was told from the first person. In this section, I added to the narrative surrounding women’s hockey in Canada by contributing my own stories from ten years of playing competitive girls’ hockey in the Greater Toronto Area

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality