We study the problem of optimal route generation for visiting measurement points in seismic surveys. For this purpose, we consider the employment of multiple drones to install seismometers at the measurement points. An algorithm combining fuzzy clustering and the traveling salesman problem is proposed to generate an energy-efficient path for each drone. Several practical conditions typically arising in this application are also taken into account.

This paper proposes a cooperative electric power supply system based on cooperative game theory, aiming to design monetary incentives for consumers to install energy resources that contribute to the continuous power supply to them in case of power interruption by disasters. The proposed system designs the incentives by maximizing the long-term profits gained by controlling photovoltaic generation systems (PVs), controllable loads, and batteries during normal operations. The proposed system employs a two-stage optimization method for scheduling battery operations under the uncertainty of PV generation during normal operations to alleviate its heavy computational burden. Since the profit allocated to each consumer participating in the proposed system is determined based on the imputation in the core, the solution of cooperative game theory, the consumers have no incentive to leave the grand coalition. When the power supply from the distribution network is interrupted during the disaster, the consumers cooperate to control their PVs and batteries to continue supplying the electric power to them using the installed resources until the power supply is restored. The effectiveness of the proposed system is verified by computational experiments under normal and disaster conditions.

Particle Swarm Optimization (PSO) is a type of evolutionary computation developed to mimic the behaviour of a flock of birds searching for food. Particles with positional information and velocity search for solutions as they move through the search space, sharing information across all particles to efficiently search for solutions. In PSO, only the positional information of the best solution is shared to update the velocity, which causes problems such as insufficient search, failure to find a global solution, and early convergence to a local solution. Sun and Li (2014) have proposed TCPSO (Two-swarm Cooperative Particle Swarm Optimization) with a slave particle swarm for intensive solution exploration. However, for high-dimensional and complex problems, even TCPSO sometimes falls into the trap of local solutions. This paper aims to improve the performance of TCPSO by using a Gaussian process to estimate the approximate shape of the function of the problem in the solution search process.

Two-dimensional layout problem is studied to minimize the wiring cost by optimal layout of the devices and wiring path connecting the devices. The present study divides the problem into a parent problem of device layout and a child problem of wiring under the given device layout and repeats to solve each problem iteratively. The decision variables of device layout are mixed with discrete variables for the direction and continuous variables for the location. Therefore, the location is discretized as target points, and each device is located at the point nearest to the given target point under the given constraints. Computer experiments are conducted to show the effectiveness of the proposed method, where simulated annealing is applied to a device layout and particle swarm optimization is applied to a wiring problem. Moreover, it is observed that the directions of the devices are gradually fixed in the descending order of the device size during the search, and the interval of the target points as the discretization size does not significantly affect the quality of the obtained solution, which is considered to be caused by the given constraints and a translational symmetry in the total wiring length under the present geometric constraints.

Finite spectrum assignment, also known as state predictive control, is an effective control method for systems with time delay in the input. This paper considers introducing some modification on the control law of state predictive control, where the modification can be interpreted, roughly speaking, as suitably taking account of the (intentionally introduced) deviation of the past input from what is desired in the sense of the conventional state predictive control. The motivation for introducing such modification lies in an attempt to modify the dynamics of the controller while maintaining a feature of the conventional state predictive control to a certain extent. In particular, we aim at improving robust stability for non-parametric uncertainties of the plant. We first derive the characteristic equation of the modified state predictive control systems, and give a necessary and sufficient condition for stability. We then derive an explicit representation of the complementary sensitivity function associated with the robust stability analysis problem for multiplicative uncertainties. Finally, we demonstrate through a numerical example that modified state predictive control can indeed be useful for improving robust stability if the modification is introduced suitably.