Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite

Yazdani, D, Omidvar, MN, Cheng, R, Branke, J, Nguyen, TT and Yao, X (2020) Benchmarking Continuous Dynamic Optimization: Survey and Generalized Test Suite. IEEE Transactions on Cybernetics. ISSN 2168-2275

[img]
Preview
Text
GMPB_accepted.pdf - Accepted Version

Download (5MB) | Preview

Abstract

Dynamic changes are an important and inescapable aspect of many real-world optimization problems. Designing algorithms to find and track desirable solutions while facing challenges of dynamic optimization problems is an active research topic in the field of swarm and evolutionary computation. To evaluate and compare the performance of algorithms, it is imperative to use a suitable benchmark that generates problem instances with different controllable characteristics. In this paper, we give a comprehensive review of existing benchmarks and investigate their shortcomings in capturing different problem features. We then propose a highly configurable benchmark suite, the generalized moving peaks benchmark, capable of generating problem instances whose components have a variety of properties such as different levels of ill-conditioning, variable interactions, shape, and complexity. Moreover, components generated by the proposed benchmark can be highly dynamic with respect to the gradients, heights, optimum locations, condition numbers, shapes, complexities, and variable interactions. Finally, several well-known optimizers and dynamic optimization algorithms are chosen to solve generated problems by the proposed benchmark. The experimental results show the poor performance of the existing methods in facing new challenges posed by the addition of new properties.

Item Type: Article
Additional Information: © 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
Uncontrolled Keywords: 0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Engineering
Publisher: IEEE
Date Deposited: 10 Aug 2020 13:24
Last Modified: 02 Sep 2020 10:15
DOI or Identification number: 10.1109/TCYB.2020.3011828
URI: http://researchonline.ljmu.ac.uk/id/eprint/13473

Actions (login required)

View Item View Item