本文選題：貓群優(yōu)化算法 + 進(jìn)化多目標(biāo)優(yōu)化算法��； 參考：《蘭州大學(xué)》2017年碩士論文

【摘要】：在生活實踐和科學(xué)研究的實際情況中,許多問題都是具備很大挑戰(zhàn)和難度且包含多個優(yōu)化目標(biāo)的多目標(biāo)優(yōu)化問題(Multi-objective Optimization Problem,MOP)。因為實際需求,多個目標(biāo)的優(yōu)化課題吸引了國內(nèi)外許多研究者的目光,并逐漸變成優(yōu)化領(lǐng)域的重點鉆研課題。進(jìn)化多目標(biāo)算法在求解包含多個目標(biāo)的優(yōu)化問題時,不但實現(xiàn)簡單而且效率較高。本文主要介紹一種比較新的進(jìn)化算法,即貓群算法(Cat Swarm Optimization,CSO),并對它進(jìn)行改進(jìn)并擴(kuò)展到多目標(biāo)領(lǐng)域,最后用它優(yōu)化包含多個目標(biāo)的問題。CSO算法的理論模型是模仿貓的行為方式創(chuàng)建的,其原理簡單易實現(xiàn),收斂速度較快,算法穩(wěn)定性較好,已被應(yīng)用于圖像處理、神經(jīng)網(wǎng)絡(luò)訓(xùn)練和模式識別等領(lǐng)域并取得了很好的效果。但是由于CSO是近些年提出的,所以理論分析和實踐應(yīng)用方面都需要進(jìn)行更深入的研究。本文在CSO中引入了精英策略,并將其擴(kuò)展為多目標(biāo),最后用它優(yōu)化一種簡單的改進(jìn)模型的脈沖耦合神經(jīng)網(wǎng)絡(luò)(Pulse-Coupled Neural Network,PCNN)參數(shù)實現(xiàn)圖像分割。具體的研究工作和創(chuàng)新點如下:1、提出了一種新的進(jìn)化多目標(biāo)優(yōu)化算法:基于Cat映射的非隨的多目標(biāo)貓群優(yōu)化算法(Non-Random Multi-Objective Cat Swarm Optimization Algorithm Based on Cat Map,NRC-MOCSO)。針對CSO算法在迭代后期極易陷入局部最優(yōu)和收斂速度緩慢的缺點,本文對CSO做了一點改進(jìn),讓種群中的貓非隨機(jī)的進(jìn)入搜索模式和跟蹤模式。再者,針對CSO算法在初始化階段種群分布不均勻而導(dǎo)致算法不穩(wěn)定性的弊端,本文利用混沌映射對種群進(jìn)行初始化,將非隨機(jī)的CSO與混沌相結(jié)合,并將其擴(kuò)展到多目標(biāo)領(lǐng)域。2、本文使用提出的NRC-MOCSO算法自動優(yōu)化一種簡單改進(jìn)模型PCNN(ISPCNN)的參數(shù)。首次實現(xiàn)了多目標(biāo)貓群算法自動優(yōu)化ISPCNN模型參數(shù)。在仿真實驗中用以熵為適應(yīng)度函數(shù)的CSO和粒子群優(yōu)化算法(Particle Swarm Optimization,PSO)、連通性為適應(yīng)度函數(shù)的CSO和PSO、熵與連通性為適應(yīng)度函數(shù)的兩個目標(biāo)的多目標(biāo)粒子群優(yōu)化算法(Multi-Objective Particle Swarm Optimization,MOPSO)和NRC-MOCSO六種方法優(yōu)化ISPCNN參數(shù),用優(yōu)化后的ISPCNN對五幅經(jīng)典的圖片進(jìn)行分割實驗。結(jié)果證明:適應(yīng)度函數(shù)對算法性能有很大的影響;針對于應(yīng)用問題多目標(biāo)比單目標(biāo)更具優(yōu)勢,能夠綜合考慮多方面的影響因素。
[Abstract]:In the practical situation of life practice and scientific research, many problems are multi-objective optimization problem with great challenge and difficulty and multi-objective optimization problem. Because of the actual demand, the multi-objective optimization project has attracted the attention of many researchers at home and abroad, and has gradually become an important research topic in the field of optimization. Evolutionary multiobjective algorithm is simple and efficient in solving optimization problems with multiple objectives. This paper mainly introduces a new evolutionary algorithm, Cat swarm optimization, and extends it to the multi-objective field. Finally, the theoretical model of CSO algorithm with multiple targets is created by imitating the behavior of cats. Its principle is simple and easy to realize, the convergence speed is faster, the algorithm is stable, and has been applied to image processing. Neural network training and pattern recognition have achieved good results. However, since CSO is proposed in recent years, theoretical analysis and practical applications need to be further studied. In this paper, elite strategy is introduced into CSO and extended to multi-objective. Finally, an improved impulse-coupled neural network named Pulse-Coupled Neural Network (PNN) is used to realize image segmentation. The specific research and innovation points are as follows: 1. A new evolutionary multi-objective optimization algorithm is proposed: Non-Random Multi-Objective Cat Optimization Algorithm based on Cat Map and Non-Random Multi-Objective Cat Optimization algorithm based on NRC-MOCSOO. In view of the shortcomings of CSO algorithm which is easy to fall into local optimum and slow convergence rate in the late iteration this paper makes some improvements to CSO so that the cats in the population can enter the search mode and the tracking mode non-randomly. Furthermore, in order to solve the problem that the population distribution of CSO algorithm is not uniform in initialization stage, the chaotic mapping is used to initialize the population, and the non-random CSO is combined with chaos. In this paper, the proposed NRC-MOCSO algorithm is used to automatically optimize the parameters of a simple improved model PCNNCMOCSO. The automatic optimization of ISPCNN model parameters based on multi-objective cat swarm algorithm is realized for the first time. Multi-Objective Particle Optimization algorithm for CSO and PSO with entropy as fitness function, CSO and PSO with connectivity as fitness function and multi-objective particle swarm optimization algorithm with entropy and connectivity as fitness function in simulation experiments Swarm Optimization / MOPSO) and NRC-MOCSO are used to optimize ISPCNN parameters. Five classic images are segmented by optimized ISPCNN. The results show that the fitness function has a great influence on the performance of the algorithm, and that the multi-objective is more advantageous than the single objective in the application of the problem, so it can comprehensively consider many factors affecting the performance of the algorithm.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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