The Cascade Optimization Algorithm: A New Distributed Approach for the Stochastic Optimization of Engineering Applications

This paper introduces a new stochastic optimization approach in the form of a cascade optimization algorithm. The algorithm incorporates concepts from Markov processes while eliminating the inherent sequential nature that is a major obstacle preventing the exploitation of advances in distributed computing infrastructures. This method introduces partitions and pools to store intermediate solutions and corresponding objectives. A Markov process increases the population of partitions and pools. The population is distributed periodically, following an external certainty. With the use of partitions and pools, multiple Markov processes can be launched simultaneously for different partitions and pools. The cascade optimization algorithm holds a potential in two different fronts. One aims at its deployment in parallel and distributed computing environments. Through storage of solutions in the pools, the algorithm further offers cost-effective means to analyze intermediate solutions, visualize progress, and integrate optimization with data and/or knowledge management techniques without additional burden to the process.