Two novel extensions for the well known Ant Colony Optimization (ACO) framework are introduced here, which allow the solution of Mixed Integer Nonlinear Programs (MINLP). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results.These extensions on the ACO framework have been developed to serve the needs of practitioners who face highly non-convex and computationally costly MINLPs. The performance of this new method is evaluated considering several non-convex MINLP benchmark problems and one real-world application. The results obtained by our implementation substantiate the success of this new approach.

Constrained optimization, Global optimization, Penalty function, Stochastic metaheuristic, Ant colony optimization, MIDACO-Solver, Mixed integer nonlinear programming (MINLP),

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Today's communication systems typically use high resolution analog-to-digital converters (ADCs). However, considering future communication systems with data rates in the order of 100 Gbit/s the ADC power consumption becomes a major factor due to the high sampling rates. A promising alternative are receivers based on 1-bit quantization and oversampling w.r.t. the signal bandwidth. Such an approach requires a redesign of modulation, receiver synchronization, and demapping. A zero crossing modulation is a natural choice as the information needs to be carried in the zero crossing time instants. The present paper provides an overview on zero crossing modulation, achievable rates, sequence mapping and demapping, 1-bit based channel parameter estimation, and continuous phase modulation as an alternative zero crossing modulation scheme.