Why do animals form swarms? In general, research into such swarms is often conducted by directly examining their movements of a swarm and presenting them as a mathematical model (in terms of differential equations) by comparing them with the movements of live animals. Although there several top-down approaches for studying animal swarms, few studies have investigated the underlying reason of emergence of swarms. In this study, we attempt to investigate this via an artificial approach based on the machine learning method. A single predator and multiple escapees are used to investigate whether or not the escapees form swarms for prolonged survival, and if so, how their swarming behavior can be modeled.

In this paper, we propose a route optimization algorithm for mixed cargo and passenger vehicles by incorporating ridesharing based on matching theory to the delivery planning problem for cargo transportation in order to reduce the total route cost in transportation. First, we consider the cargo-specific delivery route problem, which is formulated as two alternative mixed integer linear programming problems aimed at minimizing the transportation route cost by sharing the delivery tasks among multiple carriers. We then present an algorithm for determining passengers for ridesharing based on stable matching that considers the detour distance of each passenger, describe the passenger travel route search problem based on the cargo transportation route, and propose a series of algorithms for determining the optimal route of mixed cargo and passenger vehicles. Finally, the effectiveness of the proposed algorithm is evaluated through numerical simulations.

This paper proposes an algorithm for a route search problem of traveling multiple sites with the time-varying waiting time. The considering problem is typified when a guest in a crowded theme park visits attractions in a short time. The cost function is defined by three kinds of times: traveling time, service time, and waiting time. The proposed method is inspired by the “insertion and manipulation PSO strategy,” which only considers the relative traveling order. This paper introduces absolute traveling order into the algorithm to consider time-varying waiting time. Numerical experiments with theme park data verify the effectiveness of the proposed algorithm.

This note addresses the parameter estimation problem in the input matrices for Linear Time-Invariant (LTI) systems under a priori given frequency domain constraints. The constraints are incorporated into the weighted least square method with help of Generalized KYP (GKYP) lemma, the proposed method is thus formulated in terms of Linear Matrix Inequalities (LMIs). A practical example, i.e. the linearized lateral-directional motion model of an airplane, is included to demonstrate the effectiveness of our method.

In this paper, an inverse estimation method of the input from the output using the trained quaternion neural network is proposed. Training is performed on a layered quaternion neural network with neurons based on quaternion geometric operations, and the input corresponding to the output given by the trained network is estimated. By this method, the inverse problem extended to the quaternion can be solved. Inverse estimation by the proposed method is shown by the bitwise operation problems and the three-dimensional affine transformation problems.

Iterative Feedback Tuning (IFT) is a simple approach to obtain a controller that achieves the target response. However, it requires many experiments to update the parameters. In this study, we propose a new control parameter tuning method based on IFT. As a main contribution of this study, the proposed method can reduce experiment the number of the iterated experiment for parameter tuning using the data-driven estimation. Finally, we show the usefulness of the proposed method used in experimental validation.