Asynchronous optimisation with the use of a cascade search algorithm

•We propose stochastic optimisation algorithm which employs asynchronous Markov search.•Introduction of cascading of solutions and inflections enables full asynchronous operation.•Quasi-asynchronous version of conventional algorithm of Tabu Search is also presented.•Parallel operation is fully validated using complex engineering examples.

This paper introduces the development of an asynchronous approach coupled with a cascade optimisation algorithm. The approach incorporates concepts of asynchronous Markov processes and introduces a search process that is benefiting from distributed computing infrastructures. The algorithm uses concepts of partitions and pools to store intermediate solutions and corresponding objectives. Population inflections are performed periodically to ensure that Markov processes, still independent and asynchronous, make arbitrary use of intermediate solutions. Tested against complex optimisation problems and in comparison with commonly used Tabu Search, the asynchronous cascade algorithm demonstrates a significant potential in distributed operations with favourable comparisons drawn against synchronous and quasi-asynchronous versions of conventional algorithms.