the blockchain by an externally owned account-generated cryptographic signature.
# # # 4. Ethereum Database System-merkle-patricia Trie (MPT), which is a series of nodes consisting of two of the tree, at the bottom of the tree contains a large number of leaf nodes of the source data, the parent node is two child nodes of the hash value, until the root node.
# # Six. Go-ethereum Source directory Structure
"

the most important tools/p2psim provides a tool to simulate HTTP Api/puppeth creating a new Ethereum Network Wizard/rlpdump provides a formatted output of RLP data The access point of the/swarm swarm network/util provides some common tools/wnode This is a simple whisper node. It can be used as a standalone boot node. In addition, it can be used for different tes

high. You may need to use a third-party storage solution, such as Swarm or IPFs. Swarm is a distributed file storage project for Ethereum. IPFs is a non-Ethereum project, but is closely related to Ethereum; It will be used independently and can be used as an additional laye

I. Basic CONCEPTS
Particle swarm optimization (particleswarm optimization,pso), a branch of evolutionary computing, is a stochastic search algorithm simulating biological activity in nature, and it is widely used in various engineering optimization problems to find the optimal solution through cooperative mechanism in the group.
Second, the Basic principles
Fig. 1 The idea source of the algorithm
Figure 2 Location Update method for the algorithm

A history of particle swarm optimization
Particle swarm optimization (Complex Adaptive system,cas) is derived from the complex adaptive system. CAS theory was formally introduced in 1994, and CAS members are called principals. For example, the study of bird systems, in which each bird is called the subject. The subject is adaptable, it can communicate with the environment and other subjects, and change its

First visit GitHub on the Go Language development client URL: Https://github.com/ethereum/go-ethereum
Installing Ethereum Https://github.com/ethereum/go-ethereum/wiki/Building-Ethereum
Install on Mac
Brew Tap

Introduction to particle swarm algorithms
I. History of particle swarm algorithms
Particle swarm algorithms are derived from complex adaptive systems (CAS ). CAS theory was formally proposed in 1994. Members in CAs are called subjects. For example, to study a bird group system, every bird is called a subject in this system. The subject is adaptive. It can communi

Explanation of terms
Particle Swarm Optimization: Particle Swarm Optimization Theory
Stochastic optimizationtechnique: random optimization technology
EvolutionaryComputation techniques: Computer computing is used to simulate the evolutionary process of biology and evolutionary computing technology.
GeneticAlgorithms: Genetic Algorithm
TheProblem Space: The space for resolving the problem.
Fitness: fitness v

The hybrid particle swarm optimization algorithm combines the global particle swarm algorithm with the local particle swarm algorithm, and its speed is updated using the formula
where G (k+1) is the global version of the speed update formula, and L (k+1) is a local version of the speed update formula, the hybrid particle s

InIn the global standard particle swarm algorithm, the speed update of each particle changes according to two factors. The two factors are: 1. The historical optimal pI of the particle. 2. Global Optimal pg for particle populations. If you change the particle velocity update formula, update the velocity of each particle based on the following two factors: a. The particle's own historical optimal pi. B. ParticlesNeighborhoodThe optimal PNK value of the

Recently, we have to write an article about the particle swarm algorithm, so we have to implement the local version of PSO algorithm. The realization idea of the local version of particle swarm algorithm has already been described in the particle Swarm algorithm (3)----standard particle swarm algorithm (local version).

Particle swarm algorithm is mainly divided into 4 large branches:
(1) The deformation of the standard particle swarm algorithm
In this branch, the main is to the standard particle swarm optimization algorithm inertia factor, The convergence factor (constraint factor), "The Cognition" part C1, "The Society" part's C2 carries on the change and the adjustment, hop

In the global version of the standard particle swarm algorithm, the speed of each particle is updated according to two factors, the two factors are: 1. Particle own historical optimal value Pi. 2. The global optimal value of particle population pg. If you change the particle velocity update formula, let the velocity of each particle update according to the following two factors, a. Particle own historical optimal value PI. B. The optimal value of the

First, the history of particle swarm algorithm
Particle swarm algorithm originates from complex adaptive system (Complex adaptive System,cas). CAS theory was formally proposed in 1994, and members in CAs are called principals. For example, the study of bird systems, each bird in this system is called the subject. Subject has adaptability, it can communicate with environment and other subject, and change it

Particle swarm algorithm (particle SWARMOPTIMIZATION,PSO) proposed by Kennedy and Eberhart in 1995, the algorithm simulates the behavior of bird colony flying foraging, and the group achieves the optimal goal through collective collaboration, which is based on the swarm Optimization method of intelligence. Similar to the genetic algorithm, but also a group based on the iteration, but there is no genetic alg

Account
The account plays a central role in Ethereum. There are two types of accounts: external accounts (EOAS) and contract accounts. We'll focus on the external account here, which will be referred to as the account. Contract accounts are referred to as contracts and are discussed specifically in the contract chapters. The general concept of putting both external and contractual accounts into accounts is justified, since these entities are so-calle

The realization of the standard particle swarm optimization (PSO) algorithm is based on the particle Swarm algorithm (2)----the standard particle swarm optimization algorithm. It is mainly divided into 3 functions. The first function is the particle swarm initialization function
Initswarm (Swarmsize .....) ADAPTFUNC)

The content of particle swarm optimization can be obtained by searching.
The following are mainly personal understanding of particle swarm optimization, and the adjustment of weights in BP neural network
Original in: http://baike.baidu.com/view/1531379.htm
Refer to some of the contents below
===============我是引用的分界线================= 粒子根据如下的公式来更新自己的 速度和新的位置 v[] = w * v[] + c1 * rand() * (pbest[] - present

This is a creation in
Article, where the information may have evolved or changed.
swarm/cluster.goBelonging swarm package to this, it defines swarm the driver structure of the Cluster body:
// Cluster is exportedtype Cluster struct { sync.RWMutex eventHandlers *cluster.EventHandlers engines map[string]*cluster.Engine pendingEngines ma

This is a creation in
Article, where the information may have evolved or changed.
After learning from various sources, we decided to start building a private chain environment based on Ethereum Go-ethereum. Because my computer system for WIN8, in order to avoid the window environment too many inexplicable problems, deliberately through the VM built a ubuntu16.04 version of the virtual system. The following

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