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This study concerns a swarm of autonomous reactive mobile robots, qualified of naïve because of their simple constitution, having the mission of gathering objects randomly distributed while respecting two contradictory objectives: maximizing quality of the emergent heap-formation and minimizing energy consumed by aforesaid robots. This problem poses two challenges: it is a multi-objective optimization problem and it is a hard problem. To solve it, one of renowned multi-objective evolutionary algorithms is used. Obtained solution, via a simulation process, unveils a close relationship between behavioral-rules and consumed energy; it represents the sought behavioral model, optimizing the grouping quality and energy consumption. Its reliability is shown by evaluating its robustness, scalability, and flexibility. Also, it is compared with a single-objective behavioral model. Results' analysis proves its high robustness, its superiority in terms of scalability and flexibility, and its longevity measured based on the activity time of the robotic system that it integrates.

Volunteer computing (VC) has become a relatively mature technique of distributed computing. It is based on exploiting the idle time of ordinary online machines with the consent of their owners. Target applications are generally scientific projects requiring a huge amount of computational resources. Existing VC platforms raise several challenges. This work attempts to bring solutions for two defeats. The first one is the involvement of volunteers; the decreasing of participants affects the global performances. To cope with this, a new social volunteer computing environment is proposed in order to involve more volunteers. The second addressed problem is the task scheduling, which aims to optimize the use of resources. The proposed algorithm generates for each resource's class, a number of tasks whose cost of execution reflects the momentary capacity of the resources. The new solutions are validated through a theory of number's project, called “Collatz Conjecture.”