Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Abstract

Abstract-Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multi-tasking multi-objective evolutionary method to solve operational indices optimization, which involves a formulated multi-objective multifactorial operational indices optimization problem (MO-MFO) and a proposed multi-objective multifactorial optimization algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a dataset collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the multifactorial optimization algorithm, the assistant models are alternatively in multi-tasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multi-objective multifactorial optimization algorithm is applied to transfer knowledge among multi-tasking tasks. The proposed multi-tasking framework for operational indices optimization has conducted on 10 different production Conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem.Note to Practitioners−Operational indices optimization is a typical method to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multi-objective multi-tasking framework is developed to address the operational indices optimization, which includes a multi-taking multi-objective operational indices optimization problem formulation and a multi-taking multiobjective evolutionary optimization to solve the above formulated optimization problem. The proposed approach can achieve a solution set for the decision making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associate with a higher grade and yield of the product.

Off-line Data-driven Multi-objective Optimization: Knowledge Transfer between Surrogates and Generation of Final Solutions

“…In this real-world application, we are not able to validate the obtained solutions as no "real objective functions" are available for validation. For this reason, we evaluate the search ability of coarse-fine search strategy in the proposed MF-RV by comparing with the fine surrogate and the multiform optimization approach [69], which is the most recent proposed algorithm in solving the operational indices optimization problem. Note that the target accurate model in multiform approach is replaced with RBF for fair comparison.…”

A meta-heuristic algorithm for integrated optimization of dynamic resource allocation planning and production scheduling in parallel machine system

“…Meta-heuristic algorithm has been well known as one of best optimizers for the complex optimization problems. [39][40][41] NP is a new meta-heuristic algorithm introduced by Shi and Olafsson. 42 It has been successfully Index sets: N job index set, N = 1, 2, .…”

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

“…In the literature, there exist a lot of works to apply MFEA to tackle real-world problems, such as complex supply chain network management [16], bi-level optimization problem [14], double-pole balancing problem [17], composites manufacturing problem [14,18], branch testing in software engineering [19], cloud computing service composition problem [20], pollution-routing problem [21], operational indices optimization of beneficiation process [22], and time series prediction problem [23].…”

“…In contrast, a smaller value would encourage the exploitation of current solutions and speed up the population convergence. In TMO-MFEA, a larger rmp is used for diversity-related variables to enhance its diversity, while a smaller rmp is designed for convergence-related variables to achieve a better convergence [22,39].…”