基于混沌粒子群优化的图像相关匹配算法研究

该文将混沌优化搜索技术应用于粒子群优化算法(PSO),提出了一种基于混沌搜索的粒子群优化算法(CPSO),该算法利用了PSO算法的收敛快速性和混沌运动的遍历性、随机性等特点,采用混沌初始化粒子初始位置,在运行过程中根据粒子群适应度的方差来自适应混沌更新粒子位置。几种典型函数的测试结果表明:CPSO提高了对多维空间的全局搜索能力,并可以有效避免早熟现象。将该方法用于图像相关匹配算法,提出了一种新的基于CPSO的图像相关匹配算法。实验结果证明该方法对解决噪声情况下的图像匹配问题十分有效。     

基于混沌粒子群优化的图像相关匹配算法研究

该文将混沌优化搜索技术应用于粒子群优化算法（PSO）,提出了一种基于混沌搜索的粒子群优化算法（CPSO）,该算法利用了PSO算法的收敛快速性和混沌运动的遍历性、随机性等特点,采用混沌初始化粒子初始位置,在运行过程中根据粒子群适应度的方差来自适应混沌更新粒子位置。几种典型函数的测试结果表明：CPSO提高了对多维空间的全局搜索能力,并可以有效避免早熟现象。将该方法用于图像相关匹配算法,提出了一种新的基于CPSO的图像相关匹配算法。实验结果证明该方法对解决噪声情况下的图像匹配问题十分有效。     

一种新的混沌粒子群优化算法

针对粒子群优化算法(PSO)缺少跳出局部最优的机制而易出现早熟问题,提出一种新的混沌粒子群优化算法(NCPSO).该算法引入混沌扰动更新粒子的位置,避免搜索陷入局部最优,再嵌入判断早熟停滞的方法,一旦检测到早熟现象,使用逃逸策略来增大粒子群的多样性.最后用3个常用的测试函数进行仿真,实验结果表明:NCPSO算法比PSO算法、CPSO算法有更高的寻优精度和更快的收敛速度.     

继承优秀染色体片段的PSO算法求解TSP问题

粒子群优化算法(PSO)提出至今一直未能有效解决离散及组合优化问题,TSP问题是组合优化问题中一个典型的NP问题.文中参考了离散粒子群算法(DPSO)和遗传算法(GA)解决TSP问题的成功经验,提出了一种继承优秀染色体片段的PSO算法(ECFG-PSO).为避免早熟,在算法中加入了局部查找和二次初始化策略.实验证明ECFG-PSO算法解决TSP问题的效率和规模优于DPSO算法.     

求解多输入多输出检测新算法——遗传粒子群优化

设计两种基于粒子群优化算法(PSO)和基于遗传算法(GA)的多输入多输出(MIMO)系统检测算法.提出一种新的融合GA和PSO进化机制的遗传粒子群进化(GPSO)算法,并将其应用于MIMO系统检测问题求解.新算法改善了初始化种群,并将每一代粒子划为精英粒子、次优粒子和糟糕粒子三部分,对这三种粒子分别采用极值扰动、PSO...     

基于PSO算法的系统可靠性优化分析

提出了将粒子群优化算法(Patticle Swarm Optimization Algorithm,PSO)用于求解系统可靠性优化问题,建立了系统的可靠性模型,分别采用遗传算法(Genetic algonthm,GA)和PSO算法进行了优化仿真,结果表明采用PSO算法和GA算法都能实现系统可靠性优化,但是相比之下PSO算法的计算精度和求出最优解的概率更高,需较少的迭代次数,能更稳定的求解最优解,而且没有求解早熟的弱点,因此PSO算法更适合于系统可靠性优化。     

一种基于扰动项的混合粒子群优化算法

为了保持粒子种群的多样性而避免发生"早熟"的问题,本文提出一种基于扰动项混合粒子群优化算法(PSO),该方法通过提高粒子群多样性来提高PSO的收敛性能.首先用标准PSO来迭代,当粒子群失去多样性时,在包含粒子群的超球外随机设置一粒子对全局最优粒子干扰,并在PSO更新公式中加入扰动项来干扰每个粒子.最后将该改进的PSO应用于函数逼近,实验结果验证了本文提出的PSO性能优于几种经典的PSO算法.     

分集接收的STBC-MC-CDMA系统中基于PSO算法的多用户检测

该文提出了一种使用多天线分集接收的空时分组码多载波码分多址(STBC-MC-CDMA)系统中基于粒子群优化(PSO)算法的多用户检测(MUD)方案。当采用多天线分集接收时,各个天线接收的信号经历了相互独立的衰落,导致不同天线分支对应的匹配度函数相互独立。为解决多天线分集接收的多目标优化问题,提出了虚拟Pareto前端的概念,并使粒子按照Pareto优化准则进行速度和位置更新。仿真结果表明,所提方案获得了增强的开发和探索能力,其性能优于常规PSO算法和多目标遗传算法。     

考虑运输代价的制造工作流调度算法

在云制造环境下,因制造服务资源所在地域的差异性,多目标制造工作流调度不仅考虑制造服务所需时间、费用,还需考虑产品运输所需时间、费用,原有工作流调度算法无法有效优化运输代价.针对此问题,结合遗传算法全局搜索能力强与粒子群算法收敛速度快的特点,提出多目标混合遗传粒子群(MOGA - PSO)算法.仿真结果表明混合算法能够有效降低运输代价,使得工作流调度得到进一步优化,可适用于云制造环境.     

中心粒子群优化算法

在线性递减权重粒子群优化算法(LDWPSO)中提到了中心粒子这一概念,进而提出了中心粒子群优化算法(中心PSO)。在线性递减权重粒子群优化算法中,中心粒子不像其它一般的粒子,中心粒子没有明确的速度,并且被始终置于粒子群的中心。此外,在神经网络训练算法中比较中心粒子群优化算法和线性递减权重粒子群优化算法,结果表明:中心粒子群优化算法的性能优于线性递减权重粒子群优化算法。     

An improved cooperative particle swarm optimizer

Particle Swarm Optimization (PSO) is a population-based technique for optimization, which simulates the social behavior of the bird flocking, a novel Adaptive Cooperative PSO (ACPSO) with adaptive search is presented in this paper, the proposed approach combines both cooperative learning and PSO with adaptive inertia weight, cooperative learning is achieved by splitting a high-dimensional swarm into several smaller-dimensional subswarms to combat curse of dimensionality, the adaptive inertia weight is employed to control the balance of exploration and exploitation in all the smaller-dimensional subswarms, which cooperate with each other by exchanging information to determine composite fitness of the entire system. Finally, computer simulations over three benchmarks indicate that the proposed algorithm shows better convergence behavior, as compared to the Cooperative Genetic Algorithm (COGA), the PSO, and the CPSO, and then its adaptive search behavior is analyzed, demonstrating its superiority.     

Hybrid Cuckoo Search with Salp Swarm Optimization for Spectral and Energy Efficiency Maximization in NOMA System

Non-Orthogonal Multiple Access (NOMA) holds the efficiency of enabling 5G communication. Due to the faster emergence of smart devices and their correlated applications, there is a huge demand for data traffic to increase the data rate. As a result, these raising demands of the users and the restricted spectrum will minimize the energy and spectral efficiency of the wireless network. There are two major measures like spectral and energy efficiency in Fifth-Generation (5G) communication models technically analyzed in this paper. The main intent of this paper is to develop a hybrid meta-heuristic algorithm for maximizing the spectral and energy efficiency of NOMA, thus avoiding low latency communication. The proposed model integrates two well-performing meta-heuristic algorithms like Salp Swarm optimization algorithm (SSA) and Cuckoo Search Algorithm (CS) for attaining the energy and spectral efficiency maximization in NOMA. The proposed hybrid meta-heuristic algorithm called Cuckoo Levy-based SSA (CL-SSA) is developed to optimize the parameters like beamforming vectors and time allocation ratio at the base station and relay. As the conventional optimization algorithm spectral efficiency of the system reaches maximum. The mean of the proposed CL-SSA for spectral efficiency is 23%, 2.8%, 54%, 32%, and 10% increased than Cuckoo Search (CS), Salp Swarm optimization (SSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fire Fly algorithm (FF), respectively. The experimental result shows that the proposed CL-SSA maximizes the spectral efficiency and energy efficiency than conventional techniques like Cuckoo Search (CS), Salp Swarm optimization (SSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fire Fly algorithm (FF) in developed NOMA model.

     

CDMA系统粒子群多用户检测算法

Kennedy和Eberhart提出的粒子群优化算法(PSO),作为一种新的并行优化算法,在解决多维和非线性的复杂问题中,性能优良且算法简单易于实现。本文对二进制粒子群优化算法进行改进,并将其应用于DS-CDMA通信系统的多用户检测中,提出了基于矢量的二进制粒子群多用户检测器(V-BPSO-MUD),同时提出了两种高效实用的多用户检测器:基于矢量的串行二进制多用户检测器(VS-BPSO-MUD)及基于矩阵的二进制多用户检测器(M-BPSO-MUD)。仿真结果表明,PSO多用户检测器充分利用了粒子群优化算法的优良特性,性能明显优于传统的CDMA检测器,接近无多址干扰情况。     

基于自适应人工鱼群算法的多用户检测器

将智能优化算法应用到多用户检测器(MUD)问题中,是近年来改善MUD性能的一个研究方向。人工鱼群算法(AFSA)是一种新的智能优化算法,该算法具有一些遗传算法和粒子群算法不具备的特点。但是用其解决离散优化问题时,该算法保持探索与开发平衡的能力较差,且在算法运行后期搜索的盲目性较大,从而影响了该算法搜索的质量和效率。为了克服这些缺点,本文对该算法进行了改进,得到两种自适应人工鱼群算法(AAFSA_FP和AAFSA_SP),并首次用其构建了新的多用户检测器。仿真结果表明,该方法与基于遗传算法的多用户检测器和基于粒子群算法的多用户检测器相比,在误码率、抗远近效应的能力和收敛速度等方面都有明显的改善。     

Monkey King Evolution: an enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment

Optimization algorithms are proposed to maximize the desirable properties while simultaneously minimizing the undesirable characteristics. Particle Swarm Optimization (PSO) is a famous optimization algorithm, and it has undergone many variants since its inception in 1995. Though different topologies and relations among particles are used in some state-of-the-art PSO variants, the overall performance on high dimensional multimodal optimization problem is still not very good. In this paper, we present a new memetic optimization algorithm, named Monkey King Evolutionary (MKE) algorithm, and give a comparative view of the PSO variants, including the canonical PSO, Inertia Weighted PSO, Constriction Coefficients PSO, Fully-Informed Particle Sawrm, Cooperative PSO, Comprehensive Learning PSO and some variants proposed in recent years, such as Dynamic Neighborhood Learning PSO, Social Learning Particle Swarm Optimization etc. The proposed MKE algorithm is a further work of ebb-tide-fish algorithm and what’s more it performs very well not only on unimodal benchmark functions but also on multimodal ones on high dimensions. Comparison results under CEC2013 test suite for real parameter optimization show that the proposed MKE algorithm outperforms state-of-the-art PSO variants significantly. An application of the vehicle navigation optimization is also discussed in the paper, and the conducted experiment shows that the proposed approach to path navigation optimization saves travel time of real-time traffic navigation in a micro-scope traffic networks.     

Mode Converter Synthesis by the Particle Swarm Optimization

Particle Swarm Optimization (PSO) is an effective, simple and promising method intended for the fast search in multi-dimensional space [Kennedy and Eberhart, "Particle Swarm Optimization", Proc. of the 1995 IEEE International Conference on Neural Networks, 1995]. Besides special testing problems a number of engineering tasks of electrodynamics were solved by the PSO successfully [Robinson and Rahmat-Samii, "Particle Swarm Optimization in Electromagnetics", IEEE Trans. Antennas Propag., 2004; Jin and Rahmat-Samii, "Parallel Particle Swarm Optimization and Finite-Difference Time-Domain (PSO/FDTD) Algorithm for Multband and Wide-Band Patch Antenna Designs", IEEE Trans. Antennas Propag., 2005]. On the other hand, the scattering matrix technique is a fast and accurate method of mode converter analysis. We illustrate PSO by a number of converter designs developed for high-power microwaves control: a matching horn for output maser section, a corrugated converter of linear-polarized hybrid modes, a TE₀₁ mitre bend.     

Pattern synthesis of centre fed linear array using Taylor one parameter distribution and restricted search Particle Swarm Optimization

In this article, a new method of pattern synthesis of centre fed, equal distance linear array having single and multiple synthesis objectives has been proposed and statistically investigated. Single objective of reduced side lobe level (SLL) and first null beamwidth (FNBW) has been considered separately. Consequently, multiple objectives of beamwidth and side lobe level have been investigated. Synthesis of linear array for suitable objectives has been investigated on Taylor one parameter distribution with equal progressive phase. Excitation amplitude of each array element is taken as optimization parameter where distribution has been optimized using Particle Swarm Optimization (PSO) for achieving low SLL. Later the same has been incorporated for obtaining suitable FNBW. In our optimization algorithm conventional PSO has been modified with a restricted search PSO (RSPSO) where search space has been predefined within excitation amplitude range. PSO within the defined range searches for optimum excitation amplitude to achieve the desired objectives. In order to illustrate the effectiveness of the proposed RSPSO, simulation results of three significant instances of linear array have been presented for both even and odd number of element. The design results obtained using RSPSO have improved result than those obtained using other state of the art evolutionary algorithms like differential evolution (DE), invasive weeds optimization (IWO) and Conventional particle Swarm optimization (CPSO) in a statistically significant way.     

An efficient mesh router nodes placement in wireless mesh networks based on moth-flame optimization algorithm

One of the most challenging tasks is deploying a wireless mesh network backbone to achieve optimum client coverage. Previous research proposed a bi-objective function and used a hierarchical or aggregate weighted sum method to find the best mesh router placement. In this work, to avoid the fragmented network scenarios generated by previous formulations, we suggest and evaluate a new objective function to maximize client coverage while simultaneously optimizing and maximizing network connectivity for optimal efficiency without requiring knowledge of the aggregation coefficient. In addition, we compare the performance of several recent meta-heuristic algorithms: Moth-Flame Optimization (MFO), Marine Predators Algorithm (MPA), Multi-Verse Optimizer (MVO), Improved Grey Wolf Optimizer (IGWO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Harris Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Slime Mould Algorithm (SMA). We empirically examined the performance of the proposed function using different settings. The results show that our proposed function provides higher client coverage and optimal network connectivity with less computation power. Also, compared to other optimization algorithms, the MFO algorithm gives higher coverage to clients while maintaining a fully connected network.