Since the Hopfield network often suffers from being trapped in local extrema when used to solve the traveling salesman problem,this paper combines the chaotic particle swarm optimization(PSO) and Hopfield neural networks(HNN) to form a novel algorithm,CPSO-HNN.

A pricing model is developed on the basis of an independent private evaluation model and some assumptions to maximize seller\'s profits,with a CPSO(chaos particle swarm optimization) solution given to it to seek optimal prices.

This paper proposes a strategy based on Chaos Particle Swarm Optimization(CPSO),applies it into 2D Otsu method,and makes simulation experiments to compare the method and Standard PSO(SPSO).

To deal with the problem of premature convergence and slow search speed of DPSO(discrete particle swarm optimization),this paper proposes a novel hybrid discrete particle swarm optimization (HDPSO),which integrates the disturbance methods to help the particle escape from the local minima and the local algorithm PSEC to speed up the reach.

A discrete particle swarm optimization(DPSO) algorithm was put forward for multi-UCAV cooperation mission assignment problem.

This paper introduces a modified discrete particle swarm optimization(DPSO) which deals with linear order problem(LOP).

In order to improve the performance of Multi-User Detection technology and the local search capability of particle swarm optimization (PSO), clone selection algorithm (CS) is combined with traditional dispersed particle swarm optimization (DPSO), and a new advanced self-adaptation clone selection particle swarm optimization (ACSPSO) is proposed and used in Multi-User Detection.