The EURONEAR_Release1 catalog contains 1544 positions of 155 Near-Earth Asteroids, obtained betwe... more The EURONEAR_Release1 catalog contains 1544 positions of 155 Near-Earth Asteroids, obtained between 2006 and 2008. (2 data files).
Context. The EUROpean Near Earth Asteroid Research (EURONEAR) is a network which envisions to bri... more Context. The EUROpean Near Earth Asteroid Research (EURONEAR) is a network which envisions to bring some European contributions into the general context traced by the Spaceguard Foundation which was carried out during the last 15 years mainly by the US with some modest European and amateur contributions. Aims. The number of known Near Earth Asteroids (NEAs) and Potentially Hazardous Asteroids (PHAs) has increased tremendously, mainly thanks to five major surveys all focused on the discovery of new bodies. But also other facilities are required to follow-up and improvement the orbital parameters and to study the physical properties of the known bodies. These goals are better achieved by a co-ordinated network such as EURONEAR.
Context. The EUROpean Near Earth Asteroid Research (EURONEAR) is a network which envisions to bri... more Context. The EUROpean Near Earth Asteroid Research (EURONEAR) is a network which envisions to bring some European contributions into the general context traced by the Spaceguard Foundation which was carried out during the last 15 years mainly by the US with some modest European and amateur contributions. Aims. The number of known Near Earth Asteroids (NEAs) and Potentially Hazardous Asteroids (PHAs) has increased tremendously, mainly thanks to five major surveys all focused on the discovery of new bodies. But also other facilities are required to follow-up and improvement the orbital parameters and to study the physical properties of the known bodies. These goals are better achieved by a co-ordinated network such as EURONEAR.
Abstract. The paper investigate the behavior of evolutionary algorithms for solving multiobjectiv... more Abstract. The paper investigate the behavior of evolutionary algorithms for solving multiobjective combinatorial problems in dynamic environments. Present work envisages the multiobjective subset sum problem which is known as an NP-hard problem [2]. Several ...
Real-world problems often present two characteristics that are challenging to examine theoretical... more Real-world problems often present two characteristics that are challenging to examine theoretically: 1) they are dynamic (they change over time), and 2) they have multiple objectives. However, current research in the field of dynamic multi-objective optimization (DMO) is relatively sparse. In this chapter, we review recent work in this field and present our analysis of the subset sum problem in the DMO variant. Our approach uses a genetic algorithm with an external archive and a combination of Pareto dominance and aggregated fitness function. We show that the algorithm performs better on a smaller number of objectives, on type III dynamicity problems, and sometimes, counter-intuitively, on a larger data set.
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Papers by Iulia M Comsa