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A new moving peaks benchmark with attractors for dynamic evolutionary algorithms
,
Fabio Caraffini
Swarm and Evolutionary Computation, Volume: 74, Start page: 101125
Swansea University Author:
Fabio Caraffini
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© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
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DOI (Published version): 10.1016/j.swevo.2022.101125
Abstract
Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requ...
| Published in: | Swarm and Evolutionary Computation |
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| ISSN: | 2210-6502 |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60896 |
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| Abstract: |
Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requirement satisfaction problems such as Robust Optimization Over Time. The benchmark problems in these studies inherently use randomly changing parameters and therefore such randomness may make it difficult to compare these algorithms with other EDO approaches. In this paper, we introduce a new benchmark, called Moving Peaks Benchmark with Attractors, which incorporates an attractor heuristic that attracts peaks to a certain location in the environment into the moving peaks problem. The proposed benchmark is fully flexible where the dynamics of the attractors and the rate at which a peak is attracted to such attractors can be modified. By adjusting these characteristics, certain styles of movements can be achieved by a peak. We also introduce a new performance measure that focuses on the comparison of algorithms that use prediction. Seven EDO algorithms based on different working logics are chosen to give a wide representation of the state-of-the-art in this area. We argue that having predictable characteristics in the benchmark problem is more adequate for studying the performances and behaviours of those algorithms that embed prediction mechanisms. Experimental results obtained with the proposed benchmark show it’s suitability for the EDO domain as all algorithms featuring prediction capabilities display higher accuracy than their competitors. |
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| Keywords: |
Benchmark; Attractor; Evolutionary dynamic optimization; Evolutionary algorithm; Performance metric |
| College: |
Faculty of Science and Engineering |
| Start Page: |
101125 |