Novel efficient asynchronous cooperative co-evolutionary multi-objective algorithms

This article introduces asynchronous implementations of selected synchronous cooperative co-evolutionary multi-objective 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.