User pairing in cooperative wireless network coding with network performance optimization

In this paper, we consider a network-coded cooperative wireless network, where users mutually pair among themselves to realize network coding. We assume a multi-user environment, where users transmit to a common destination in the absence of dedicated relaying nodes. We address the important problem of the mutual pairing of users, which directly governs the overall network performance. An optimal user pairing algorithm is proposed and tailored to maximize the network capacity. Next, we develop heuristic user pairing schemes, which demonstrate near-optimal performance at significantly reduced computational complexity. In particular, we propose max-max pairing to maximize the network capacity and max-min pairing to minimize the outage probability. We then consider power minimization for energy-constrained networks. A joint optimization problem is formulated and solved to find the pairing which maximizes the network capacity and minimizes the transmission power, while meeting certain network performance constraint, such as in terms of the minimum average capacity per user or maximum average outage probability per user

A Simplex Method-Based Salp Swarm Algorithm for Numerical and Engineering Optimization

Part 4: Neural Computing and Swarm IntelligenceInternational audienceSalp Swarm Algorithm (SSA) is a novel meta-inspired optimization algorithm. The main inspiration of this algorithm is the swarming behavior of salps when navigating and foraging in the ocean. This algorithm has already displayed the strong ability in solving some engineering design problems. This paper proposes an improved salp swarm algorithm based on simplex method named as simplex method-based salp swarm algorithm (SMSSA). The simplex method is a stochastic variant strategy, which increases the diversity of the population and enhances the local search ability of the algorithm. This approach helps to achieve a better trade-off between the exploration and exploitation ability of the SSA and makes SSA more robust and faster. The proposed algorithm is compared with other four meta-inspired algorithms on 4 benchmark functions. The proposed algorithm is also applied to one real-life constrained engineering design problems. The experimental results have demonstrated the MSSSA performs better than the other competitive meta-inspired algorithms