A multitask optimization algorithm based on elite individual transfer.

Evolutionary multitasking algorithms aim to solve several optimization tasks simultaneously, and they can improve the efficiency of various tasks evolution through the knowledge transfer between different optimization tasks. Evolutionary multitasking algorithms have been applied to various applications and achieved certain results. However, how to transfer knowledge between tasks is still a problem worthy of research.

A Rough-to-Fine Evolutionary Multiobjective Optimization Algorithm.

This article presents a rough-to-fine evolutionary multiobjective optimization algorithm based on the decomposition for solving problems in which the solutions are initially far from the Pareto-optimal set. Subsequently, a tree is constructed by a modified k -means algorithm on N uniform weight vectors, and each node of the tree contains a weight vector. Each node is associated with a subproblem with the help of its weight vector.

An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization.

Multiobjective multitasking optimization (MTO), which is an emerging research topic in the field of evolutionary computation, was recently proposed. MTO aims to solve related multiobjective optimization problems at the same time via evolutionary algorithms. The key to MTO is the knowledge transfer based on sharing solutions across tasks.

Objective-Domain Dual Decomposition: An Effective Approach to Optimizing Partially Differentiable Objective Functions.

This paper addresses a class of optimization problems in which either part of the objective function is differentiable while the rest is nondifferentiable or the objective function is differentiable in only part of the domain. Accordingly, we propose a dual-decomposition-based approach that includes both objective decomposition and domain decomposition. In the former, the original objective function is decomposed into several relatively simple subobjectives to isolate the nondifferentiable part of the objective function, and the problem is consequently formulated as a multiobjective optimization problem (MOP).

[Study on brand traceability of vinegar based on near infrared spectroscopy technology].

In the present paper, 152 vinegar samples with four different brands were chosen as research targets, and their near infrared spectra were collected by diffusion reflection mode and transmission mode, respectively. Furthermore, the brand traceability models for edible vinegar were constructed. The effects of the collection mode and pretreatment methods of spectrum on the precision of traceability models were investigated intensively.

[Studies on the brand traceability of milk powder based on NIR spectroscopy technology].

Brand traceability of several different kinds of milk powder was studied by combining near infrared spectroscopy diffuse reflectance mode with soft independent modeling of class analogy (SIMCA) in the present paper. The near infrared spectrum of 138 samples, including 54 Guangming milk powder samples, 43 Netherlands samples, and 33 Nestle samples and 8 Yili samples, were collected. After pretreatment of full spectrum data variables in training set, principal component analysis was performed, and the contribution rate of the cumulative variance of the first three principal components was about 99.