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

Blockchain Introduction
A blockchain is a data structure that is logically linked from the back to the block that contains the transaction information. It can be stored as flat file, which contains files with no relative relational records, or stored in a simple database. Bitcoin core clients use Google's LEVELDB database to store blockchain metadata. Chunks are

block Chain Intro
What the blockchain really is.
Blockchain (English: Blockchain) is a distributed database, originated from Bitcoin, Blockchain is a series of cryptographic methods associated with the generated data block, each block contains a number of Bitcoin network transactions information, to verify the validi

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

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The blockchain uses an Internet-based (Internet)-on-Peer (peer-to-peer) network architecture. Peer to peer refers to each computer located in the same network is equal to each other, each node provides network services together, there is no "special" node. Each network node is connected with a "flat (flat)" topology. There is no service-side (server), centralized service,

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

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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

December 9 Afternoon of the BDTC 2017 blockchain and database sub-forum, from East China Normal University, Chinese Academy of Sciences, fun chain, people's College, People's Insurance, Microsoft, a number of experts, from the academic, industry, currency and other perspectives, together to explore the blockchain technology, industry status and development. Here is a selection of highlights: Five faces of t

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

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

If the consensus mechanism is the core of the soul of the blockchain, then for the blockchain, especially the alliance chain and private chain, cross-linking technology is the key to realize the value network, it is to save the alliance chain from the separate isolated islands to rescue the medicine, is the blockchain outward expansion and connection of the bridg

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