In this paper a new probability-based multi-valued Particle Swarm Optimization algorithm is developed for problems with nominal variables. The algorithm more explicitly contextualizes probability updates in terms of roulette wheel probabilities. It is first applied to three forest planning problems, and its performance is compared to the performance of a forest planning algorithm, as well as to results from other algorithms in other papers. The new algorithm outperforms the others except in a single case, where a customized forest planning algorithm obtains superior results. Finally, the new algorithm is compared to three other probability-based Particle Swarm Optimization algorithms via analysis of their probability updates. Three intuitive but fully justified requirements for generally effective probability updates are presented, and through single iteration and balance convergence tests it is determined that the new algorithm violates these requirements the least.