模擬追逐算法及其應(yīng)用研究
發(fā)布時(shí)間：2018-07-23 16:20
【摘要】：智能優(yōu)化算法是通過模擬自然生物進(jìn)化或者社會(huì)行為而提出的一種隨機(jī)搜索算法.這些算法具有原理簡(jiǎn)單、模型易于實(shí)現(xiàn)的特征,能夠解決許多傳統(tǒng)方法難以解決的復(fù)雜問題,被廣泛應(yīng)用于各個(gè)領(lǐng)域.本文借鑒長(zhǎng)跑比賽運(yùn)動(dòng)員追逐競(jìng)賽的過程,建立了數(shù)學(xué)模型并應(yīng)用于優(yōu)化問題的求解,提出了一種新的智能優(yōu)化算法——模擬追逐算法(Simulated Pursuit Algorithm).模擬追逐算法融合了試探性開拓與有目的性的追逐相結(jié)合的優(yōu)點(diǎn),是一種有效的群體全局優(yōu)化算法.本文首先介紹基本模擬追逐算法,分析了算法的基本原理;為提高算法種群多樣性,引入?yún)f(xié)作算子,提出了協(xié)同進(jìn)化的模擬追逐算法;為將模擬追逐算法應(yīng)用從連續(xù)優(yōu)化問題推廣到離散優(yōu)化問題,對(duì)探測(cè)算子與追逐算子給出了新的設(shè)計(jì)定義,提出一種改進(jìn)的模擬追逐算法求解TSP問題.具體研究工作如下三個(gè)方面:1、提出了新的群體智能算法——模擬追逐算法.該算法設(shè)計(jì)了追逐算子與探測(cè)算子;領(lǐng)先個(gè)體執(zhí)行探測(cè)算子操作以便獲得更優(yōu)位置,落后個(gè)體為取得競(jìng)爭(zhēng)優(yōu)勢(shì),設(shè)定追趕目標(biāo),執(zhí)行追逐算子操作,完成跟隨超越,從而實(shí)現(xiàn)群體進(jìn)化尋優(yōu).對(duì)追逐算子進(jìn)行性能分析;采用六個(gè)典型的測(cè)試函數(shù)進(jìn)行仿真實(shí)驗(yàn),分析了算法的求解精度、收斂速度以及穩(wěn)定性.仿真實(shí)驗(yàn)表明,模擬追逐算法有較快的收斂速度和較高的求解精度,是一種穩(wěn)定的優(yōu)化算法.2、為了維持算法搜索過程中種群多樣性,本文在基本的模擬追逐算法(SPA)中引入三種協(xié)作算子,充分共享個(gè)體間的信息,提出一種協(xié)同進(jìn)化模擬追逐算法.四個(gè)基準(zhǔn)函數(shù)測(cè)試表明,在算法后期加入翻轉(zhuǎn)交叉算子可避免產(chǎn)生過多的重復(fù)解;改進(jìn)后的算法平衡了搜索的集中性和種群多樣性,提高算法跳出局部最優(yōu)的能力;協(xié)同進(jìn)化模擬追逐算法在尋優(yōu)能力與收斂速度均優(yōu)于基本的模擬追逐算法.3、為將模擬追逐算法推廣應(yīng)用到組合優(yōu)化問題,本文提出了一種改進(jìn)的模擬追逐算法求解TSP.算法采用貪心策略與對(duì)稱策略初始化種群;定義交換運(yùn)算、交換矩陣,對(duì)追逐算子和探測(cè)算子給出新的設(shè)計(jì)定義;仿真實(shí)驗(yàn)表明,改進(jìn)的模擬追逐算法對(duì)TSP有更高的求解精度,是一種有效算法.模擬追逐算法應(yīng)用于求解TSP問題的研究,提供了將模擬追逐算法運(yùn)應(yīng)用于處理離散優(yōu)化問題的模板,擴(kuò)寬了模擬追逐算法的應(yīng)用領(lǐng)域.
[Abstract]:Intelligent optimization algorithm is a random search algorithm proposed by simulating natural evolution or social behavior. These algorithms have the characteristics of simple principle, easy to implement the model, and can solve many complex problems which are difficult to be solved by traditional methods. They are widely used in various fields. In this paper, using the process of long distance race for reference, a mathematical model is established and applied to solve the optimization problem. A new intelligent optimization algorithm, simulation chase algorithm (Simulated Pursuit Algorithm)., is proposed in this paper. Simulation chase algorithm, which combines the advantages of exploratory exploration and purposeful pursuit, is an effective group global optimization algorithm. In this paper, the basic simulation chase algorithm is introduced, and the basic principle of the algorithm is analyzed, in order to improve the diversity of the algorithm population, the cooperative operator is introduced, and the simulation chase algorithm of co-evolution is proposed. In order to extend the application of simulation chase algorithm from continuous optimization problem to discrete optimization problem, a new design definition of detection operator and chase operator is given, and an improved simulation chase algorithm is proposed to solve the TSP problem. The specific research work is as follows: 1. A new swarm intelligence algorithm-simulation chase algorithm is proposed. In this algorithm, chase operator and detection operator are designed, leading individual performs detection operator operation in order to obtain better position, backward individual sets chase target, executes chase operator operation, and accomplishes following surpassing in order to gain competitive advantage. Thus, the optimization of population evolution can be realized. The performance of the chase operator is analyzed, and six typical test functions are used to simulate the algorithm. The accuracy, convergence speed and stability of the algorithm are analyzed. The simulation results show that the simulation chase algorithm has faster convergence speed and higher accuracy, and is a stable optimization algorithm. In order to maintain the diversity of the population in the search process of the algorithm, In this paper, three kinds of cooperative operators are introduced into the basic simulation chase algorithm (SPA) to fully share the information between individuals, and a co-evolution simulation chasing algorithm is proposed. The test of four benchmark functions shows that adding overturning crossover operator in the later stage of the algorithm can avoid too many repetitive solutions and the improved algorithm balances the search centrality and population diversity and improves the ability of the algorithm to jump out of the local optimum. In order to extend the simulation chase algorithm to combinatorial optimization problem, an improved simulation chase algorithm is proposed to solve the TSPs in order to extend the simulation chase algorithm to the combinatorial optimization problem. The algorithm uses greedy strategy and symmetric strategy to initialize the population, define exchange operation, exchange matrix, give a new design definition of chase operator and detection operator, and the simulation results show that the improved simulation chase algorithm has higher accuracy for TSP. Is an effective algorithm. The simulation chase algorithm is applied to the study of solving the TSP problem. It provides a template for the simulation chase algorithm to deal with discrete optimization problems and widens the application field of the simulation chase algorithm.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18
本文編號(hào)：2139956
[Abstract]:Intelligent optimization algorithm is a random search algorithm proposed by simulating natural evolution or social behavior. These algorithms have the characteristics of simple principle, easy to implement the model, and can solve many complex problems which are difficult to be solved by traditional methods. They are widely used in various fields. In this paper, using the process of long distance race for reference, a mathematical model is established and applied to solve the optimization problem. A new intelligent optimization algorithm, simulation chase algorithm (Simulated Pursuit Algorithm)., is proposed in this paper. Simulation chase algorithm, which combines the advantages of exploratory exploration and purposeful pursuit, is an effective group global optimization algorithm. In this paper, the basic simulation chase algorithm is introduced, and the basic principle of the algorithm is analyzed, in order to improve the diversity of the algorithm population, the cooperative operator is introduced, and the simulation chase algorithm of co-evolution is proposed. In order to extend the application of simulation chase algorithm from continuous optimization problem to discrete optimization problem, a new design definition of detection operator and chase operator is given, and an improved simulation chase algorithm is proposed to solve the TSP problem. The specific research work is as follows: 1. A new swarm intelligence algorithm-simulation chase algorithm is proposed. In this algorithm, chase operator and detection operator are designed, leading individual performs detection operator operation in order to obtain better position, backward individual sets chase target, executes chase operator operation, and accomplishes following surpassing in order to gain competitive advantage. Thus, the optimization of population evolution can be realized. The performance of the chase operator is analyzed, and six typical test functions are used to simulate the algorithm. The accuracy, convergence speed and stability of the algorithm are analyzed. The simulation results show that the simulation chase algorithm has faster convergence speed and higher accuracy, and is a stable optimization algorithm. In order to maintain the diversity of the population in the search process of the algorithm, In this paper, three kinds of cooperative operators are introduced into the basic simulation chase algorithm (SPA) to fully share the information between individuals, and a co-evolution simulation chasing algorithm is proposed. The test of four benchmark functions shows that adding overturning crossover operator in the later stage of the algorithm can avoid too many repetitive solutions and the improved algorithm balances the search centrality and population diversity and improves the ability of the algorithm to jump out of the local optimum. In order to extend the simulation chase algorithm to combinatorial optimization problem, an improved simulation chase algorithm is proposed to solve the TSPs in order to extend the simulation chase algorithm to the combinatorial optimization problem. The algorithm uses greedy strategy and symmetric strategy to initialize the population, define exchange operation, exchange matrix, give a new design definition of chase operator and detection operator, and the simulation results show that the improved simulation chase algorithm has higher accuracy for TSP. Is an effective algorithm. The simulation chase algorithm is applied to the study of solving the TSP problem. It provides a template for the simulation chase algorithm to deal with discrete optimization problems and widens the application field of the simulation chase algorithm.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP18
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