Strengthening evolution-based differential evolution with prediction strategy for multimodal optimization and its application in multi-robot task allocation

Zhao, Hong; Tang, Lin; Li, Jia Rui; Liu, Jing

doi:10.1016/j.asoc.2023.110218

Cited by 8 publications

(1 citation statement)

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“…There might be some cases in which the problem requires retrieving all optimal solutions (both local and global). Then, there are additional methodologies that can be employed: the crowding model [101], the sharing function [102], specific strategies such as adding a further objective that minimizes the derivative of the first objective (i.e., such that if the derivative reaches zero, the solution is a local optimum) and retrieves weakly dominated solutions with NSGA-II [44], or innovative algorithms such as the strengthening evolution-based differential evolution with prediction strategy [103].…”

Section: Multi Modalitymentioning

confidence: 99%

Optimizing Exoskeleton Design with Evolutionary Computation: An Intensive Survey

Stroppa,

Soylemez,

Yuksel

et al. 2023

Robotics

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Exoskeleton devices are designed for applications such as rehabilitation, assistance, and haptics. Due to the nature of physical human–machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of exoskeletons. We completed the search in the IEEE, ACM, and MDPI databases between 2017 and 2023 using the keywords “exoskeleton”, “design”, and “optimization”. We categorized our findings in terms of which limb (i.e., hand, wrist, arm, or leg) and application (assistive, rehabilitation, or haptic) the exoskeleton was designed for, the optimization metrics (force transmission, workspace, size, and adjustability/calibration), and the optimization method (categorized as evolutionary computation or non-evolutionary computation methods). We discuss our observations with respect to how the optimization methods have been implemented based on our findings. We conclude our paper with suggestions for future research.

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Section: Multi Modalitymentioning

confidence: 99%