Abstract: Following recently published socio-cognitively inspired ACO concept for global optimization, we try to verify the proposed idea by adapting the PSO in a similar way. The swarm is divided into species and the particles get inspired not only by the global and local optima, but share the knowledge about the optima with neighbourhood agents belonging to other species. After presenting the concept and motivation, the experimental results gathered for common benchmark functions tackled in 100 dimensions are shown and the efficacy of the proposed algorithm is discussed.

Abstract: In swarm robotics, it is necessary to develop methods and strategies that guide the collective execution of tasks by the robots. The design of such tasks can be done considering it as a collection of simpler behaviors, called subtasks. In this paper, the Wave Swarm is presented as a general strategy to manage the sequence of subtasks that compose the collective navigation , which is an important task in swarm robotics. The proposed strategy is based mainly on the execution of wave algorithms. The swarm is viewed as a distributed system, wherein the communication is achieved by message passing among robot’s neighborhood. Message propagation delimits the start and end of each subtask. Simulations are performed to demonstrate that controlled navigation of robot swarms/clusters is achieved with three subtasks, which are recruitment, alignment and movement.

Abstract: The growing share of LED light sources in outdoor lighting enables developing street lighting solutions characterized by high energy efficiency. It is accomplished by replacing high intensity discharging lamps with LEDs and implementing various control strategies. It was also shown that a well tailored lighting design may significantly decrease the power usage. To apply this method in large projects, however, the computationally efficient approach is necessary. In this article we propose the method of energy efficiency optimization relying on a multi-agent system framework which enables scalable computations capable of handling large-scale projects. The case of a real-life optimization is also presented in the paper.

Abstract: We study the features of a new mixed integration scheme dedicated for solving the nonstationary variational problems. The scheme is composed of the FEM approximation with respect to the space variable coupled with a 3-layered time integration scheme with a linearized right-hand side operator. It was applied in solving the Cahn-Hilliard parabolic equation with a nonlinear, fourth-order elliptic part. The second order of the approximation along the time variable was proven. Moreover, the good scalability of the software based on this scheme was confirmed during simulations. We verify the proposed time integration scheme by monitoring the Ginzberg-Landau free energy. The numerical simulations are performed using parallel multifrontal direct solver executed over STAMPEDE Linux cluster. Its scalability was compared to the results of the three direct solvers, including MUMPS, SuperLU and PaSTiX.