A brief introduction to particle swarm optimization algorithm 2--particle swarm optimization _ particle swarm

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

Three, the basic formula of the algorithm

The speed of the algorithm is composed of three parts: (1) its own speed, (2) individual cognition, and (3) social guidance

Iv. algorithm flow and pseudo-code

The research and improvement direction of the algorithm

1. Algorithm theory Research

2. Research on algorithm parameters

3. Research on topological structure

4. Hybrid Algorithm Research

5, Algorithm application research

Vi. Learning Resources

1. Important Academic journals and international conferences

Lieeetransactions on evolutionary computation lieee transactions in Systems, Manand cybernetics lieee on ... l Machine Learning levolutionary Computation

ANTS

Internationalworkshop on Ant Colony optimization and Swarm Intelligence

GECCO (International Conference on Evolutionary Computing)

Geneticand Evolutionary Computation Conference

Particleswarm optimization (PSO), that is focuses on continuousoptimization problems.
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2. Important meetings
L Ieeecongress on Evolutionary Computation L IEEE International Conference Onsystems, Mans, and Cybernetics (SMC) L-A CM Genetic and Evolutionarycomputation Conference (GECCO) L International Conference on Antcolony optimization and Swarm I Ntelligence (ANTS) L International Conference onsimulated Evolution and Learning (SEAL) () (