本文關(guān)鍵詞：求解優(yōu)化問題的改進(jìn)粒子群算法研究　出處：《北方民族大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 粒子群算法 無約束優(yōu)化問題 混合整數(shù)優(yōu)化問題 多目標(biāo)優(yōu)化問題

【摘要】：粒子群優(yōu)化(PSO)算法由于原理簡單、易實現(xiàn)、調(diào)控參數(shù)少及收斂性能好等優(yōu)點,并且能夠有效解決不連續(xù)、不可微的優(yōu)化問題,已經(jīng)成為智能優(yōu)化領(lǐng)域的研究熱點;但PSO算法存在收斂速度慢、精度低及不易跳出局部尋優(yōu)等缺點.本文提出了粒子的最近等值粒子和最優(yōu)反方向概念,并且對無約束連續(xù)優(yōu)化問題、混合整數(shù)問題和多目標(biāo)優(yōu)化問題提出了相應(yīng)的改進(jìn)算法.首先,簡述了優(yōu)化問題的相關(guān)概念、研究現(xiàn)狀和求解方法,闡述了目前常見的群智能優(yōu)化算法及研究狀況,主要對粒子群優(yōu)化算法的相關(guān)原理進(jìn)行了分析.其次,提出了粒子的最近等值粒子,并在此基礎(chǔ)上提出了等高隨機(jī)替換策略,運用簡化粒子群算法進(jìn)行更新,加快了粒子尋優(yōu)能力;同時對適應(yīng)值最差的一部分粒子,采用了最優(yōu)隨機(jī)反方向搜索策略以利于跳出局部最優(yōu).對不同類型的測試函數(shù),仿真實驗表明,兩種策略的融合使得算法在尋優(yōu)速度和精度方面上有極大的提高.再次,針對混合整數(shù)優(yōu)化問題,提出了多維慣性權(quán)重的改進(jìn)方法,使得粒子群算法中的每個粒子都可以有選擇的對自身上代速度進(jìn)行學(xué)習(xí);同時采用線性遞減步長搜索策略,平衡了整數(shù)變量的全局和局部搜索能力;采用混沌策略對品質(zhì)最差的部分粒子進(jìn)行變異,從而保證了種群多樣性.實驗數(shù)據(jù)驗證了改進(jìn)策略的有效性.最后,針對多目標(biāo)優(yōu)化問題,提出了多維自適應(yīng)的慣性權(quán)重.同時引入了擁擠熵策略進(jìn)行外部檔案的維護(hù)和更新,并對算法中距離最優(yōu)粒子較遠(yuǎn)的部分粒子采取混沌變異策略,從而保證了種群多樣性.比較實驗數(shù)據(jù)證實了算法的可行性和有效性.
[Abstract]:Particle Swarm Optimization (PSO) algorithm has the advantages of simple principle, easy implementation, less control parameters and good convergence performance, and it can effectively solve the problem of discontinuous and non-differentiable optimization. It has become the research hotspot in the field of intelligent optimization. However, the PSO algorithm has the disadvantages of slow convergence, low precision and difficulty to jump out of the local optimization. In this paper, the concepts of the nearest equivalent particle and the optimal inverse direction of particles are proposed, and the unconstrained continuous optimization problem is also discussed. The corresponding improved algorithms for mixed integer problem and multi-objective optimization problem are proposed. Firstly, the related concepts, research status and solution methods of the optimization problem are briefly described. This paper describes the current common swarm intelligence optimization algorithm and its research status, mainly analyzes the relevant principles of particle swarm optimization algorithm. Secondly, the recent equivalent particles of particles are proposed. On the basis of this, a random substitution strategy of equal height is proposed, and the simplified particle swarm optimization algorithm is used to update the algorithm, which accelerates the ability of particle optimization. At the same time, for some particles with the worst fitness, the optimal random reverse direction search strategy is used to jump out of the local optimum. The simulation results for different types of test functions show that. The fusion of the two strategies greatly improves the speed and accuracy of the algorithm. Thirdly, for the mixed integer optimization problem, an improved method of multi-dimension inertia weight is proposed. Each particle in the particle swarm optimization algorithm can be selected to learn the previous generation speed. At the same time, the global and local search ability of integer variables is balanced by linear decreasing step size search strategy. Chaos strategy is used to mutate some of the worst-quality particles so as to ensure the diversity of the population. The experimental data verify the effectiveness of the improved strategy. Finally, the multi-objective optimization problem is addressed. A multidimensional adaptive inertial weight is proposed, and the congestion entropy strategy is introduced to maintain and update the external files, and chaos mutation strategy is adopted for some particles which are far away from the optimal particles in the algorithm. Thus, the diversity of the population is guaranteed, and the feasibility and effectiveness of the algorithm are verified by comparing the experimental data.
【學(xué)位授予單位】：北方民族大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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