Title: Efficient parameter-free adaptive penalty method with balancing the objective function value and the constraint violation

Authors: Takeshi Kawachi; Jun-ichi Kushida; Akira Hara; Tetsuyuki Takahama

Addresses: Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asaminami-Ku, Hiroshima, 731-3194, Japan ' Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asaminami-Ku, Hiroshima, 731-3194, Japan ' Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asaminami-Ku, Hiroshima, 731-3194, Japan ' Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asaminami-Ku, Hiroshima, 731-3194, Japan

Abstract: Real world problems are often formularised as constrained optimisation problems (COPs). Constraint handling techniques are important for efficient search, and various approaches such as penalty methods or feasibility rule have been studied. The penalty methods deal with a single fitness function by combining an objective function value and a constraint violation with a penalty factor. Moreover, the penalty factor can be flexibly adapted by feeding back information on search process in adaptive penalty methods. However, setting parameters properly and keeping the good balance between the objective function value and the constraint violation are difficult. In this paper, we propose a new parameter-free adaptive penalty method with balancing the objective function value and the constraint violation. L-SHADE is adopted as a base search algorithm, and the optimisation results of 28 benchmark functions provided by the CEC2017 and CEC2018 competitions on constrained single-objective numerical optimisations are compared with other methods.

Keywords: evolutionary algorithms; differential evolution; constraint handling techniques; penalty method.

DOI: 10.1504/IJCISTUDIES.2021.115426

International Journal of Computational Intelligence Studies, 2021 Vol.10 No.2/3, pp.127 - 160

Received: 21 Mar 2020
Accepted: 06 Jul 2020

Published online: 19 May 2021 *

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