Guest editorial: Memetic Algorithms for Evolutionary Multi-Objective Optimization

Abstract

This thematic issue on Memetic Algorithms for Evolutionary Multi-Objective Optimization was initially motivated by the success of Memetic Algorithms (MAs) in a variety of real-world problems. Among them, the multi-objective class of problems is of great interest. This issue brings together some of the latest research developments in this field, with two papers highlighting some of the fundamental algorithmic issues and another paper focusing on a specific real-world application. The paper by Gong et al. proposed a memetic algorithm to address static multi-objective optimization problems. The proposed algorithm, namely Multi-objective Lamarckian ImmuneAlgorithm (MLIA), employs the general framework of Non-dominated Neighbor Immune Algorithm. Lamarckian learning was embedded in this framework as the local search method. Empirical studies showed that, with the incorporation of Lamarckian learning, MLIA achieved very appealing performance in comparison with some state-ofthe-art multi-objective evolutionary algorithms, such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). Dynamic optimization has attracted significant research interests in recent years. In their work, Wang and Li