An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework

The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex multiobjective optimization problems. However, the multiobjective artificial bee colony (MOABC) algorithm has not been integrated into the common multiobjective optimization frameworks which provide the integrated environments for understanding, reusing, implementation, and comparison of multiobjective algorithms. Therefore, a unified, flexible, configurable, and user-friendly MOABC algorithm framework is presented which combines a multiobjective ABC algorithm named RMOABC and the multiobjective evolution algorithms (MOEA) framework in this paper. The multiobjective optimization framework aims at the development, experimentation, and study of metaheuristics for solving multiobjective optimization problems. The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application. The experiment’s results showed the framework can deal with practical multiobjective optimization problems more effectively and flexibly, can provide comprehensive and reliable parameters sets, and can complete reference, comparison, and analysis tasks among multiple optimization algorithms

Hybrid Statistical and Engineering Optimization Architectures in Early Multidisciplinary Designs of Resilience and Expensive Black-box Complex Systems

Practical engineering design problems are generally multi-disciplinary with limited budget and high risk in terms of life loss, economic resources, etc. In the early phase of such problems, selection of true efficient designs is desired while minimizing overall design cost by avoiding expensive search processes. However, the task is difficult for a simple optimization framework due to the formulation complexity, high function evaluation cost, uncertain design parameters etc. Thus, the overall research goal is to develop complex, hybrid optimization architectures for solving early design problems considering the trade-off among model complexity, performance and cost. We start by comparing multiple architectures, and investigated a nested bi-level architecture for early resilience design with discrete design space and with a trade-off among multiple objectives at different risk level scenarios. The work then focused on increased problem complexity with black-box functions in a mechanical design classification problem with discontinuous design space using a sequential Bayesian Optimization (BO) architecture to locate an unknown creep-fatigue failure constraint boundary. The work then extends a weighted Tchebycheff black-box multi-objective BO (MO-BO) architecture for mechanical design with a trade-off between design risk and cost, with model calibration through regression analysis of unknown parameters. Finally, we investigate an iterative regression model selection procedure, nested into the proposed MO-BO, to enhance design flexibility, estimation and overall performance. This work can be applicable to any domains of complex or/and expensive black-box system design problems