We propose a novel approach for combinatorial optimization problems. For solving the traveling salesman problems, we combine chaotic neurodynamics with heuristic algorithm. We select the heuristic algorithm of 2-opt as a basic part, because it is well understood that this simple algorithm is very effective for the traveling salesman problems. Although the conventional approaches with chaotic neurodynamics were only applied to such very small problems as 10 cities, our method exhibits higher performance for larger size problems with the order of 1$0^2$.

Reinforcement learning involves decision-making in dynamic and uncertain environments and constitutes a crucial element of artificial intelligence. In our previous work, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers can be used to efficiently solve the two-armed bandit problem, which requires decision-making concerning a class of difficult trade-offs called the exploration–exploitation dilemma. However, only two selections were employed in that research; hence, the scalability of the laser-chaos-based reinforcement learning should be clarified. In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time series. The experimental demonstrations in which bandit problems with up to 64 arms were successfully solved are presented where laser chaos time series significantly outperforms quasiperiodic signals, computer-generated pseudorandom numbers, and coloured noise. Detailed analyses are also provided that include performance comparisons among laser chaos signals generated in different physical conditions, which coincide with the diffusivity inherent in the time series. This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.

In order to enable the benefits of device-to-device (D2D) communications underlay cellular networks, two fundamental problems need to be addressed: 1) how to control the interference to guarantee the quality-of-service (QoS) of cellular user and 2) how to allocate transmit powers among D2D pairs to maximize the data rate. In this letter, we propose a distributed resource allocation to resolve these problems. Specifically, the interference from D2D transmissions to cellular users is coordinated using a pricing scheme, while a D2D pair competes with other pairs to efficiently reuse the available spectrum. We model this competition using a non-cooperative power control game and propose a distributed update rule to reach the Nash equilibrium. Simulation is provided to evaluate the performance of the proposed algorithm.

Cognitive radio technology improves radio resource usage by reconfiguring the wireless connection settings according to the optimum decisions, which are made on the basis of the collected context information. This paper focuses on optimization algorithms for decision making to optimize radio resource usage in heterogeneous cognitive wireless networks. For networks with centralized management, we proposed a novel optimization algorithm whose solution is guaranteed to be exactly optimal. In order to avoid an exponential increase of computational complexity in large-scale wireless networks, we model the target optimization problem as a minimum cost-flow problem and find the solution of the problem in polynomial time. For the networks with decentralized management, we propose a distributed algorithm using the distributed energy minimization dynamics of the Hopfield-Tank neural network. Our algorithm minimizes a given objective function without any centralized calculation. We derive the decision-making rule for each terminal to optimize the entire network. We demonstrate the validity of the proposed algorithms by several numerical simulations and the feasibility of the proposed schemes by designing and implementing them on experimental cognitive radio network systems.