Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147964
Title: Cognizant multitasking in multiobjective multifactorial evolution : MO-MFEA-II
Authors: Bali, Kavitesh Kumar
Gupta, Abhishek
Ong, Yew-Soon
Tan, Puay Siew
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Bali, K. K., Gupta, A., Ong, Y. & Tan, P. S. (2020). Cognizant multitasking in multiobjective multifactorial evolution : MO-MFEA-II. IEEE Transactions On Cybernetics, 51(4), 1784-1796. https://dx.doi.org/10.1109/TCYB.2020.2981733
Project: A19C1a0018 
Journal: IEEE Transactions on Cybernetics 
Abstract: Humans have the ability to identify recurring patterns in diverse situations encountered over a lifetime, constantly understanding relationships between tasks and efficiently solving them through knowledge reuse. The capacity of artificial intelligence systems to mimic such cognitive behaviors for effective problem solving is deemed invaluable, particularly when tackling real-world problems where speed and accuracy are critical. Recently, the notion of evolutionary multitasking has been explored as a means of solving multiple optimization tasks simultaneously using a single population of evolving individuals. In the presence of similarities (or even partial overlaps) between high-quality solutions of related optimization problems, the resulting scope for intertask genetic transfer often leads to significant performance speedup-as the cost of re-exploring overlapping regions of the search space is reduced. While multitasking solvers have led to recent success stories, a known shortcoming of existing methods is their inability to adapt the extent of transfer in a principled manner. Thus, in the absence of any prior knowledge about the relationships between optimization functions, a threat of predominantly negative (harmful) transfer prevails. With this in mind, this article presents a realization of a cognizant evolutionary multitasking engine within the domain of multiobjective optimization. Our proposed algorithm learns intertask relationships based on overlaps in the probabilistic search distributions derived from data generated during the course of multitasking-and accordingly adapts the extent of genetic transfers online. The efficacy of the method is substantiated on multiobjective benchmark problems as well as a practical case study of knowledge transfers from low-fidelity optimization tasks to substantially reduce the cost of high-fidelity optimization.
URI: https://hdl.handle.net/10356/147964
ISSN: 2168-2267
DOI: 10.1109/TCYB.2020.2981733
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TCYB.2020.2981733.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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