research-article

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

Authors:

Mohammad Nabi Omidvar,

Xin YaoAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 26, Issue 5

Pages 823 - 843

https://doi.org/10.1109/TEVC.2021.3130835

Published: 01 October 2022 Publication History

Abstract

This article is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely, decomposition methods and hybridization methods, such as memetic algorithms and local search. In this part, we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multiobjective optimization, constraint handling, overlapping components, the component imbalance issue and benchmarks, and applications. The article also includes a discussion on pitfalls and challenges of the current research and identifies several potential areas of future research.

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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 26, Issue 5

Oct. 2022

398 pages

ISSN:1089-778X

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https://dl.acm.org/doi/10.1109/TEVC.2023.3306017
Zhong RYu J(2024)Gene-targeting multiplayer battle game optimizer for large-scale global optimization via cooperative coevolutionCluster Computing10.1007/s10586-024-04600-627:9(12483-12508)Online publication date: 1-Dec-2024
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