A generic scheme is proposed for designing and classifying simple probabilistic population based optimization algorithms that use principles from Population-based Ant Colony Optimization and Simplified Swarm Optimization for solving combinatorial optimization problems. The scheme, called Simple Probabilistic Population Based Optimization scheme, identifies different types of populations (or archives) and their influence on the construction of new solutions. The scheme is used to show how Simplified Swarm Optimization can be adapted for solving combinatorial optimization problems and how it is related to Population-based Ant Colony Optimization. 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 behaviour of Simple Probabilistic Population Based Optimization algorithms, when applied to the Traveling Salesperson Problem and the Quadratic Assignment Problem.
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