Data-Driven Surrogate-Assisted Multi-Objective Optimization of Complex Beneficiation Operational Process

TL;DR: This paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, meetamodeling techniques, state-of-the-art meetingamodels-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms.

TL;DR: The results show that the Pareto-optimal solutions at higher profits (with lower resource utilization rates) are more sensitive to the unit copper concentrate prices than those obtained in regions with lower profits.

TL;DR: A ‘multi-objective optimization model’ based on a ‘fast and elitist Non-dominated Sorting Genetic Algorithm’ and ‘Artificial Neural Networks’ (ANN) for the optimization of production technical indicators in the entire geology, mining and beneficiation metal mine production processes is proposed.

TL;DR: The paper suggests increasing the efficiency of managerial decisions by means of implementing intellectual add-in to the existing automated control system based on the optimization mathematical model of control over ore concentrate production process.

TL;DR: The problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front are studied to enable researchers to test their algorithms for specific aspects of multi- objective optimization.

TL;DR: In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space, and reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization.

TL;DR: This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

TL;DR: A comprehensive survey of the research on fitness approximation in evolutionary computation is presented, main issues like approximation levels, approximate model management schemes, model construction techniques are reviewed and open questions and interesting issues in the field are discussed.