Adaptive Control of Subpopulations in Evolutionary Dynamic Optimization

Yazdani, Danial; Cheng, Ran; He, Cheng; Branke, Jürgen

doi:10.1109/tcyb.2020.3036100

Cited by 10 publications

(3 citation statements)

References 44 publications

(88 reference statements)

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“…For example, in [6,13], a very simple single-population restart-based PSO (RPSO) from [53,75] is used. It is shown in [76][77][78] that this EDOA is ineffective in optimizing many DOPs. This inefficiency of RPSO is not surprising, as this EDOA is constructed by only integrating a simple changereaction-based global diversity control component, which randomizes a predefined portion of the inferior individuals after environmental changes, to a single population PSO 10 .…”

Section: Multi-objectivizationmentioning

confidence: 99%

“…This way, however, may be prohibitive or ineffective when the available computational resources are limited in each environment (e.g., problems with high change frequency). This ineffectiveness would be a consequence of the additional usage of the computational resources (i.e., fitness evaluation burden) by the sampling process, while the optimization component is usually struggling with the shortage of the available computational resources to perform sufficient exploration/exploitation [78].…”

Section: Multi-objectivizationmentioning

confidence: 99%

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Robust Optimization Over Time: A Critical Review

Yazdani,

Omidvar,

Yazdani

et al. 2024

IEEE Trans. Evol. Computat.

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Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, limitations on the resources required to deploy new solutions, and/or the system's inability to tolerate frequent changes in the deployed solutions. ROOT is dedicated to the study and development of algorithms capable of dealing with the implications of deploying or maintaining solutions over longer time horizons involving multiple environmental changes. This paper presents an in-depth review of the research on ROOT. The overarching aim of this survey is to help researchers gain a broad perspective on the current state of the field, what has been achieved so far, and the existing challenges and pitfalls. This survey also aims to improve accessibility and clarity by standardizing terminology and unifying mathematical notions used across the field, providing explicit mathematical formulations of definitions, and improving many existing mathematical descriptions. Moreover, we classify ROOT problems based on two ROOT-specific criteria: the requirements for changing or keeping deployed solutions and the number of deployed solutions. This classification helps researchers gain a better understanding of the characteristics and requirements of ROOT problems, which is crucial to systematic algorithm design and benchmarking. Additionally, we classify ROOT methods based on the approach they use for finding robust solutions and provide a comprehensive

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Section: Multi-objectivizationmentioning

confidence: 99%

Section: Multi-objectivizationmentioning

confidence: 99%

Robust Optimization Over Time: A Critical Review

Yazdani,

Omidvar,

Yazdani

et al. 2024

IEEE Trans. Evol. Computat.

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“…Increasing the number of components results in a larger number of promising regions that may contain the global optimum after environmental changes. Therefore, dynamic optimization algorithms (DOAs) that try to locate and track multiple moving promising regions [14] will face difficulties in tackling such problems. In fact, covering larger numbers of promising regions is more computational resource consuming which is challenging due to the limited available computational resources in each environment.…”

Section: Problem Instancesmentioning

confidence: 99%

Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark

Omidvar,

Yazdani,

Branke

et al. 2021

Preprint

Self Cite

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This document describes the generalized moving peaks benchmark (GMPB) [1] and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set 15 benchmark problems, the relevant source code, and a performance indicator, designed for comparative studies and competitions in large-scale dynamic optimization. Although its primary purpose is to provide a coherent basis for running competitions, its generality allows the interested reader to use this document as a guide to design customized problem instances to investigate issues beyond the scope of the presented benchmark suite. To this end, we explain the modular structure of the GMPB and how its constituents can be assembled to form problem instances with a variety of controllable characteristics ranging from unimodal to highly multimodal, symmetric to highly asymmetric, smooth to highly irregular, and various degrees of variable interaction and ill-conditioning.

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