粒子群优化算法是一种新型、高效的进化计算方法。
Particle swarm optimization algorithm is a new and efficient evolutionary computation method.
通过这种处理使得粒子群体的进化速度加快，从而提高了算法的收敛速度和精度。
The particle swarm optimization speeded up the evolution process, and improved the convergence speed and accuracy.
粒子群优化算法(PSO)是一种进化计算技术，是一种基于迭代的优化工具。
Particle Swarm optimization (PSO) is an evolutionary computation technique and an optimization tool based on iteration.
利用粒子群算法和差分进化算法的优点，可以获得测向问题的全局最优解。
The ADPSO is a global optimization algorithm for direction finding, which takes advantage of the merits of differential evolutionary algorithm and particle swarm algorithm.
文章将量子进化算法(QEA)和粒子群算法(PSO)互相结合，提出了两种混合量子进化算法。
Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid Quantum Evolutionary algorithms (QEA) based on combining QEA with Particle Swarm optimization (PSO).
这类算法主要包括进化算法(EA)、粒子群算法(PSO)、人工免疫系统(ais)和蚁群算法(aco)等等。
Such algorithms include evolutionary algorithm (EA), particle swarm optimization (PSO), artificial immune system (AIS) and ant colony optimization (ACO) and so on.
本文作者还结合记忆策略、差异进化算法和粒子群优化算法提出记忆进化算法(MCOEA)。
Constrained optimization evolutionary algorithm based on memory, which integrates particle swarm optimization (PSO) with differential evolution (DE), named MCOEA, is proposed.
本文将进化算法引入飞机排班问题，研究并实现了基于离散型粒子群算法的飞机排班系统。
This thesis introduces Discrete Particle Swarm Optimizer (DPSO) Algorithm to solve the fleet assignment problem. Firstly, the characterization of the fleet assignment is analyzed.
本文将进化算法引入飞机排班问题，研究并实现了基于离散型粒子群算法的飞机排班系统。
This thesis introduces Discrete Particle Swarm Optimizer (DPSO) Algorithm to solve the fleet assignment problem. Firstly, the characterization of the fleet assignment is analyzed.
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