Homogeneous Parallel Island Models (HoPIMs) run the same bio-inspired algorithm (BA) in all islands. Several communication topologies and migration policies have been fine-tuned in such models, speeding up and providing better quality solutions than sequential BAs for different case studies. This work selects four HoPIMs that successfully ran a genetic algorithm (GA) in all their islands. Furthermore, it proposes and studies the performance of heterogeneous versions of such models (HePIMs) that run four different BAs in their islands, namely, GA, double-point crossover GA, Differential Evolution, and Particle Swarm Optimization. HePIMs aim to maintain population diversity covering the space of solutions and reducing the overlap between islands. The NP-hard evolutionary reversal distance problem is addressed with HePIMs verifying their ability to compute accurate solutions and outperforming HoPIMs.