Abstract: Hybridizing agent-based paradigm with evolutionary or memetic computation can enhance the field of meta-heuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones. In the article, an evolutionary multi-agent system (EMAS) is applied to solve difficult discrete benchmark problems without any domain-specific knowledge---thus they may be called ``black-box'' ones. As a means for comparison, a parallel evolutionary algorithm (constructed along with Michalewicz model) versus evolutionary and memetic versions of EMAS are used. The obtained results point out that EMAS is significantly more efficient than classical evolutionary algorithms and also finds better results in the examined problem instances.

Abstract: This paper presents design, implementation and performance results of a new modular, parallel, agent-based and large scale crowd simulation environment. A parallel application, implemented with C and MPI, was implemented and run in this parallel environment for simulation and visualization of an evacuation scenario at Gdansk University of Technology, Poland and further in the area of districts of Gdansk. The application uses a parallel MPI I/O run on two different clusters and a two or three node Parallel File System (PFS) to store a current state in a file. In order to make this implementation efficient, we used our previously developed and tuned Byte-addressable Non-volatile RAM Distributed Cache - a solution that allows to access small data chunks from spread locations within a file efficiently. We have presented application execution times versus the number of agents (up to 100000), versus the number of simulation iterations (up to 25000), versus map size (up to 6km^2) and versus the number of processes (up to more than 650) showing high speed-ups.

Abstract: This paper presents some of the techniques, algorithms and designs used to enable mass evacuation simulations to take advantage of high performance computing infrastructure. A brief overview of a tsunami mass evacuation simulator capable of simulating urban areas of hundreds of km2 in sub-meter detail is provided. Enhancements to the serial algorithms for path finding reducing the path finding time in 94% and a cache friendly visual boundary extraction algorithm cutting the overall simulation time in 50% are presented. Furthermore the hybrid parallel (distributed memory (MPI) + shared memory (OpenMP)) framework is described. A dynamic load balancing technique reducing the idling time from 50% of the execution time to 3% is presented. Finally measures of the thread parallel strong scalability up to 16 threads of 82.69% and distributed process strong scalability up to 2048 processes of 75.93% are presented.

Abstract: Volunteer computing is a very appealing way of utilizing vast available resources in an efficient way. However currently available platforms supporting this computing style are either difficult to use or not available at all, being the results of e.g. finished scientific projects. In this paper a novel, lightweight volunteer computing platform is presented. In order to contribute the resources to this platform, only a web-browser is required without the need to install any additional plugins or other software. In this paper, besides general considerations and presentation of the platform structure and functionalities, selected results proving its efficiency are shown.

Abstract: Agent-based modelling (ABM) is a widely recognized simulation technique that is particularly relevant for economic, environmental, security and humanitarian scenarios [1,2]. Within this work we focus particularly on escape scenarios. On this topic, ABMs have been extensively applied to model events such as fire escapes [2-4], evacuations from large scale disasters [5-7], or the movements of refugees in times of conflict [8-9]. Unfortunately, many of these ABM simulation and model validation studies are constrained by limitations in the available empirical data. Most commonly, there is insufficient empirical information to validate ABMs in any complete sense [10], a phenomenon which is further exacerbated by ABMs containing a relatively rich set of adjustable parameters. In some cases, these limitations are so extreme that they hinder the construction of these models. For example, in the case of refugee modelling, people are only registered by the UNHCR once they have reached a safe location (i.e., a refugee camp, see data.unhcr.org), yet the modellers wish to know how many people reside in the area of danger, and at what times. Similar mismatches occur in simulations of fire escape or disaster relief, when registrations and recordings are only made in safe locations. Using the registration data from safe locations directly to determine the number of people in areas of danger results in a structural underestimation of the number of occupants in safe locations, as the agents in the simulation require time to travel from the area of danger to a safe haven [9]. One common method to reduce these kind of errors is by permitting a warmup time in the simulation (see e.g., [11]). However, in escape scenarios such warmup times only eliminate these errors altogether if the time required for agents to arrive at the safe location is constant across all agents, and known in advance. In this talk I will present a timescale realignment technique that allows researchers to use safe location data as an input parameter for refugee escape simulations. Using this technique it is possible to fully eliminate the validation error in total safe location registrations, given that the number of safe location registrations remains positive, and at the expense of introducing an additional error in travel speeds. References: [1] J Doyne Farmer and Duncan Foley. The economy needs agent-based mod- elling. Nature, 460(7256):685–686, 2009. [2] Eric Bonabeau. Agent-based modeling: Methods and techniques for sim- ulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3):7280–7287, 2002. [3] Timo Korhonen, Simo Hostikka, Simo Heli ̈ovaara, and Harri Ehtamo. Fds+ evac: an agent based fire evacuation model. In Pedestrian and Evacuation Dynamics 2008, pages 109–120. Springer, 2010. [4] Fangqin Tang and Aizhu Ren. Agent-based evacuation model incorpo- rating fire scene and building geometry. Tsinghua Science & Technology, 13(5):708–714, 2008. [5] Xuwei Chen, John W Meaker, and F Benjamin Zhan. Agent-based mod- eling and analysis of hurricane evacuation procedures for the florida keys. Natural Hazards, 38(3):321–338, 2006. [6] Jianyong Shi, Aizhu Ren, and Chi Chen. Agent-based evacuation model of large public buildings under fire conditions. Automation in Construction, 18(3):338–347, 2009. [7] AS Mordvintsev, VV Krzhizhanovskaya, MH Lees, and PMA Sloot. Sim- ulation of city evacuation coupled to flood dynamics. In Pedestrian and Evacuation Dynamics 2012, pages 485–499. Springer International Pub- lishing, 2014. [8] J. A. Sokolowski, C. M. Banks, and R. L. Hayes. Modeling population displacement in the syrian city of aleppo. In Proceedings of the 2014 Winter Simulation Conference, pages 252–263. IEEE Press, 2014. [9] D. Groen. Simulating refugee movements: Where would you go? Procedia Computer Science, 80:2251–2255, 2016. [10] Andrew Crooks, Christian Castle, and Michael Batty. Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6):417–430, 2008. [11] Marek Laskowski and Shamir Mukhi. Agent-based simulation of emer- gency departments with patient diversion. In International Conference on Electronic Healthcare, pages 25–37. Springer, 2008.