Simple Probabilistic Population-Based Optimization
提出一个通用框架（SPPBO）来设计和分类简单的基于概率的群体优化算法，结合群体蚁群优化和简化群体优化的原理，用于解决组合优化问题，并通过旅行商问题和二次分配问题的实验评估不同群体类型的影响。
A generic scheme is proposed for designing and classifying simple probabilistic population-based optimization (SPPBO) algorithms that use principles from population-based ant colony optimization (PACO) and simplified swarm optimization (SSO) for solving combinatorial optimization problems. The scheme, called SPPBO, identifies different types of populations (or archives) and their influence on the construction of new solutions. The scheme is used to show how SSO can be adapted for solving combinatorial optimization problems and how it is related to PACO. Moreover, several new variants and combinations of these two metaheuristics are generated with the proposed scheme. An experimental study is done to evaluate and compare the influence of different population types on the optimization behavior of SPPBO algorithms, when applied to the traveling salesperson problem and the quadratic assignment problem.