We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.

Multi-strategy Differential Evolution / Yaman, Anil; Iacca, Giovanni; Coler, Matt; Fletcher, George; Pechenizkiy, Mykola. - 10784:(2018), pp. 617-633. (Intervento presentato al convegno EvoApplications 2018 tenutosi a Parma nel 4th-6th April 2018) [10.1007/978-3-319-77538-8_42].

Multi-strategy Differential Evolution

Iacca, Giovanni;
2018-01-01

Abstract

We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.
2018
Applications of Evolutionary Computation
Cham
Springer
978-3-319-77537-1
978-3-319-77538-8
Yaman, Anil; Iacca, Giovanni; Coler, Matt; Fletcher, George; Pechenizkiy, Mykola
Multi-strategy Differential Evolution / Yaman, Anil; Iacca, Giovanni; Coler, Matt; Fletcher, George; Pechenizkiy, Mykola. - 10784:(2018), pp. 617-633. (Intervento presentato al convegno EvoApplications 2018 tenutosi a Parma nel 4th-6th April 2018) [10.1007/978-3-319-77538-8_42].
File in questo prodotto:
File Dimensione Formato  
yaman.pdf

Open Access dal 01/01/2020

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 329.22 kB
Formato Adobe PDF
329.22 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/204712
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 7
social impact