Technological advancement has made robots become rampant in industrial automation and globalization, as it’s fast efficient delivering tasks industries with no supervision. This work shows the use of blockchain technology (smart contract) controlling a swarm combined particle optimization for solving navigation path. The technique this research modeled new fitness function, that uses optimal pa...

In this paper, we propose a novel optimistic unchoking approach for the BitTorrent protocol whose key objective is to improve the quality of inter-connections amongst peers. In turn, this yields enhanced data distribution without penalizing underutilized and/or idle peers. The suggested policy takes into consideration the number of peers currently interested in downloading from a client that is...

A common issue amongst security researchers is the lack of publicly available network traffic traces. In this paper we present Chappie Swarm, which seeks to emulate human behavior in regard to internet browsing. The experimenter can unleash a number of automated chappies which will assume pre-defined personas, and then actively go out and query websites while simultaneously recording their brow...

Diversity control in the particle swarm optimization (PSO) algorithm is one of the important issues that influence the process of finding global optimal solution. In this study we create a historical process to find best area of the search space for population dispersion guide on PSO algorithm, and name Diversity Guided Particle Swarm Optimization algorithm (DGPSO) algorithm. Hence we propose a...

We present a biologically inspired approach to the dynamic assignment and reassignment of a swarm of homogeneous robots to multiple locations, with applications in search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between mult...

Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior ...