Babalik, AhmetOzkis, AhmetUymaz, Sait AliKiran, Mustafa Servet2020-03-262020-03-2620181568-49461872-9681https://dx.doi.org/10.1016/j.asoc.2018.04.009https://hdl.handle.net/20.500.12395/36250In this study, the authors focus on modification of the artificial algae algorithm (AAA), for multi-objective optimization. Basically, AAA is a population-based optimization algorithm inspired by the behavior of microalgae cells. In this work, a modified AAA with appropriate strategies is proposed for multi-objective Artificial Algae Algorithm (MOAAA) from the first AAA that was initially presented to solve single-objective continuous optimization problems. To the best of our knowledge, the MOAAA is the first modification of the AAA for solving multi-objective problems. Performance of the proposed algorithm is examined on a benchmark set consisting of 36 different multi-objective optimization problems and compared with four different swarm intelligence or evolutionary algorithms that are well-known in literature. The MOAAA is highly successful in solving multi-objective problems, and it has been demonstrated that the MOAAA is an alternative competitive algorithm in multi-objective optimization according to experimental results and comparisons presented in this research topic. (C) 2018 Elsevier B.V. All rights reserved.en10.1016/j.asoc.2018.04.009info:eu-repo/semantics/closedAccessArtificial algae algorithmMulti-objective optimizationNon-dominated sortingArticle68377395Q1WOS:000433155300028Q1