基本介绍
(多图聚类)GA-HIDMSPSO优化DBSCAN密度聚类优化算法，matlab代码，超多图
基于遗传算法辅助异构改进的动态多群粒子群优化算法（GA-HIDMSPSO）优化DBSCAN密度聚类，matlab代码，直接运行！
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1、参数epsilon：这是一个距离参数，定义了一个点的邻域的半径。在这个半径内，点的密度被计算。简单来说，epsilon 确定了每个点周围的“搜索范围”
2、参数minPts：这是一个密度阈值，定义了在 epsilon 半径内至少需要有多少个点，才能把该点标记为核心点（core point）。如果一个点的 epsilon 半径内的点的数量大于或等于 minPts，那么这个点是一个核心点，否则是边界点（border point）或噪声点（noise point）
3、最佳对数似然值：最大对数似然值用于评估聚类模型对数据的拟合程度。较大的对数似然值表明模型更好地拟合了数据，即数据在该模型下出现的概率较大
图例（超多图，满足paper需求）
1、GA-HIDMSPSO优化后的密度聚类图
2、GA-HIDMSPSO收敛曲线
3、聚类类型分布饼状图
4、优化参数之间的三维立体图
5、对数似然值变化曲线
6、epsilon参数分布直方图
7、minPts参数分布直方图
GA-HIDMSPSO算法原理
算法是一种将最先进的粒子群优化算法(PSO)与遗传算法(GA)相结合的元启发式优化算法。该混合模型利用了HIDMS-PSO的异构特性和遗传算法的进化特性。在遗传算法-HIDMS-PSO体系结构中，HIDMS-PSO作为主要搜索引擎，遗传算法作为辅助方法，对HIDMS-PSO算法的同质和异质亚群的选定比例进行辅助和减缓多样性的丧失。这两种方法连续运行。作为主要的搜索方法，HIDMS-PSO比遗传算法运行时间更长。HIDMS-PSO从同质和异质亚群中为遗传算法提供初始解，遗传算法返回的最终解取代了HIDMS-PSO中的先前解，从而恢复了搜索过程，并可能使用更多样化的粒子来引导群体。
The algorithm described is a meta-heuristic optimization algorithm that combines the state-of-the-art Particle Swarm Optimization (PSO) with Genetic Algorithm (GA). This hybrid model leverages the heterogeneous characteristics of Heterogeneous Inertia-Driven Multi-Swarm PSO (HIDMS-PSO) and the evolutionary traits of Genetic Algorithm. In the GA-HIDMS-PSO architecture, HIDMS-PSO serves as the primary search engine, while Genetic Algorithm acts as an auxiliary method to assist and mitigate the loss of diversity among selected proportions of homogeneous and heterogeneous subpopulations within the HIDMS-PSO algorithm. These two methods operate consecutively.
As the primary search method, HIDMS-PSO runs for a longer duration compared to Genetic Algorithm. HIDMS-PSO provides initial solutions to Genetic Algorithm from both homogeneous and heterogeneous subpopulations. The final solutions returned by Genetic Algorithm replace the previous solutions in HIDMS-PSO, thereby revitalizing the search process and potentially guiding the swarm with more diversified particles.
Here’s a concise summary of the key points:
Combination of Algorithms: The algorithm integrates HIDMS-PSO and Genetic Algorithm.
Roles: HIDMS-PSO is the primary search engine, while Genetic Algorithm serves as an auxiliary method.
Subpopulation Management: HIDMS-PSO manages homogeneous and heterogeneous subpopulations.
Diversity Preservation: Genetic Algorithm helps mitigate the loss of diversity in HIDMS-PSO.
Continuous Operation: The two methods operate continuously, with HIDMS-PSO running longer.
Solution Exchange: HIDMS-PSO provides initial solutions to Genetic Algorithm, and the improved solutions from Genetic Algorithm are used to replace old solutions in HIDMS-PSO.
Search Process Revival: This exchange helps revitalize the search process with more diversified particles.
This hybrid approach leverages the strengths of both HIDMS-PSO and Genetic Algorithm, potentially leading to more efficient and effective optimization solutions.
