This article introduces asynchronous implementations of selected synchronous cooperative co-evolutionary multiobjective evolutionary algorithms (CCMOEAs). The CCMOEAs chosen are based on the following state-of-the-art multi-objective evolutionary algorithms (MOEAs): Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). The cooperative co-evolutionary variants presented in this article differ from the standard MOEAs architecture in that the population is split into islands, each of them optimizing only a sub-vector of the global solution vector, using the original multi-objective algorithm. Each island evaluates complete solutions through cooperation, i.e., using a subset of the other islands current partial solutions. We propose to study the performance of the asynchronous CCMOEAs with respect to their synchronous versions and base MOEAs on well kown test problems, i.e. ZDT and DTLZ. The obtained results are analyzed in terms of both the quality of the Pareto front approximations and computational speedups achieved on a multicore machine.

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This paper introduces two new nominal NK Landscape model instances designed to mimic the properties of one challenging optimisation problem from biology: the Inverse Folding Problem (IFP), here focusing on a simpler secondary structure version. Through landscape analysis tests, numerous problem properties are identified and used to parameterise and validate model instances in terms of epistatic links, adaptive-and random walk characteristics. Then the performance of different Genetic Algorithms (GAs) is compared on both the new NK Models and the original IFP, in terms of population diversity, solution quality and convergence characteristics. It is demonstrated that very similar properties are captured in all presented tests with a significantly faster evaluation time compared to the real IFP. The future purpose of such a model is to provide a generic benchmark for algorithms targeting protein sequence optimisation, specifically in protein design. It may also provide the foundation for more in-depth studies of the size, shape and characteristics of the solution space of good solutions to the IFP.