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Explorations in swarm algorithms: Hybrid particle swarm optimization and adaptive culture model algorithms.

機(jī)譯：群算法的探索：混合粒子群優(yōu)化和自適應(yīng)文化模型算法。

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Swarm Intelligence refers to the approach of performing a complex task with a large number of simple agents following simple rules. This dissertation proposes two new swarm optimization algorithms namely the gradient based particle swarm optimization (GPSO) algorithm and the continuous adaptive culture model (CACM) algorithm. The GPSO algorithm and the CACM algorithm are then shown to be computationally efficient and converge faster than existing swarm optimization techniques.; Stochastic optimization algorithms like genetic algorithms (GA) and simulated annealing (SA) algorithm perform global optimization but waste computational effort by doing a random search. On the other hand, deterministic algorithms like gradient descent converge rapidly but may get stuck in local minima of multimodal functions. Thus, an approach that combines the strengths of stochastic and deterministic optimization schemes but avoids their weaknesses is of interest. This dissertation presents a new hybrid gradient based PSO (GPSO) algorithm that converges to a significantly more accurate solution than existing PSO based techniques for a variety of test functions.; The utility of the GPSO algorithm is demonstrated by applying it to a target location estimation problem in wireless sensor networks (WSNs). It was observed that the use of the GPSO algorithm provides significantly higher position estimation accuracy throughout the sensor field than classical deterministic schemes.; Optimization of large scale problems cannot be done in reasonable time by serial machines, thus schemes for parallel implementation are of interest. The classical PSO algorithm uses the global best solution to update each particle which leads to unacceptably high communication levels in parallel implementations. This dissertation explores new PSO schemes which do not use the global best solution to update each particle, thereby avoiding communication bottlenecks inherent in the serial PSO algorithm.; GAs are typically used for optimization of discontinuous and nondifferentiable functions that cannot be optimized with derivative based techniques. However, GAs are computation intensive and suffer from slow convergence rates. A new computationally inexpensive alternative to GAs, the continuous adaptive culture model (CACM), is proposed in this dissertation and shown to perform competitively on a variety of benchmark test functions from the literature.
機(jī)譯：群智能是指按照簡(jiǎn)單規(guī)則用大量簡(jiǎn)單代理執(zhí)行復(fù)雜任務(wù)的方法。本文提出了兩種新的群體優(yōu)化算法，即基于梯度的粒子群優(yōu)化算法（GPSO）和連續(xù)自適應(yīng)文化模型（CACM）算法。與現(xiàn)有的群優(yōu)化技術(shù)相比，GPSO算法和CACM算法具有更高的計(jì)算效率和收斂速度。遺傳算法（GA）和模擬退火（SA）算法等隨機(jī)優(yōu)化算法執(zhí)行全局優(yōu)化，但是通過(guò)進(jìn)行隨機(jī)搜索會(huì)浪費(fèi)計(jì)算量。另一方面，確定性算法（例如梯度下降）會(huì)迅速收斂，但可能會(huì)陷入多峰函數(shù)的局部最小值。因此，一種將隨機(jī)優(yōu)化和確定性優(yōu)化方案的優(yōu)點(diǎn)相結(jié)合但避免其缺點(diǎn)的方法引起了人們的興趣。本文提出了一種新的基于混合梯度的PSO（GPSO）算法，該算法可收斂到比現(xiàn)有的基于PSO的多種測(cè)試功能技術(shù)更為精確的解決方案。通過(guò)將GPSO算法應(yīng)用于無(wú)線傳感器網(wǎng)絡(luò)（WSN）中的目標(biāo)位置估計(jì)問(wèn)題，證明了其實(shí)用性。已經(jīng)觀察到，與傳統(tǒng)的確定性方案相比，GPSO算法的使用在整個(gè)傳感器領(lǐng)域提供了更高的位置估計(jì)精度。串行機(jī)器無(wú)法在合理的時(shí)間內(nèi)完成大規(guī)模問(wèn)題的優(yōu)化，因此人們對(duì)并行實(shí)現(xiàn)方案感興趣。經(jīng)典的PSO算法使用全局最佳解決方案來(lái)更新每個(gè)粒子，這在并行實(shí)現(xiàn)中導(dǎo)致無(wú)法接受的高通信級(jí)別。本文探索了不使用全局最優(yōu)解來(lái)更新每個(gè)粒子的PSO方案，從而避免了串行PSO算法固有的通信瓶頸。 GA通常用于優(yōu)化不可用基于導(dǎo)數(shù)的技術(shù)優(yōu)化的不連續(xù)和不可微函數(shù)。但是，GA的計(jì)算量大，收斂速度慢。本文提出了一種新的計(jì)算成本低廉的遺傳算法替代方法，即連續(xù)自適應(yīng)培養(yǎng)模型（CACM），并證明該方法在文獻(xiàn)中的各種基準(zhǔn)測(cè)試功能上具有競(jìng)爭(zhēng)力。

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  • 作者

    Noel, Mathew M.;

  • 作者單位

    The University of Alabama at Birmingham.;

  • 授予單位 The University of Alabama at Birmingham.;
  • 學(xué)科 Engineering General.
  • 學(xué)位 Ph.D.
  • 年度 2005
  • 頁(yè)碼 85 p.
  • 總頁(yè)數(shù) 85
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類(lèi) 工程基礎(chǔ)科學(xué);
  • 關(guān)鍵詞

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