A Multi-Population Electromagnetic Algorithm for Dynamic Optimisation Problems Ayad Mashaan Turky and Salwani Abdullah ayadalrashid@gmail.com, salwani@ftsm.ukm.my Abstract: This paper is derived from an interest in the development of approaches to tackle dynamic optimisation problems. This is a very challenging research area due to the fact that any approaches utilised should be able to track the changes and simultaneously seek for global optima as the search progresses. In this research work, a multi-population electromagnetic algorithm for dynamic optimisation problems is proposed. An electromagnetic algorithm is a population based meta-heuristic method which imitates the attraction and repulsion of the sample points. In order to track the dynamic changes and to effectively explore the search space, the entire population is divided into several sub- populations (referred as multi-population that acts as diversity mechanisms) where each sub- population takes charge in exploring or exploiting the search space. In addition, further investigation are also conducted on the combination of the electromagnetic algorithm with different diversity mechanisms (i.e. random immigrants, memory mechanism and memory based immigrant schemes) with the aim of identifying the most appropriate diversity mechanism for maintaining the diversity of the population in solving dynamic optimisation problems. The proposed approach has been applied and evaluated against the latest methodologies in reviewed literature of research works with respect to the benchmark problems. This study demonstrates that the electromagnetic algorithm with a multi- population diversity mechanism performs better compared to other population diversity mechanisms investigated in our research and produces some of the best known results when tested on Moving Peak Benchmark (MPB) problems. Keywords: Multi-population based method, Electromagnetic algorithm, Dynamic optimisation problems 1. Introduction Most of the real world optimisation problems are dynamic in nature i.e., the problem parameters are either revealed or changed during the search progress. Their vital application makes them an on-going challenging problem for the research community. Unfortunately, optimisation methods that have been used to solve static problems face difficulties to directly apply on dynamic one. This is because the problem keeps changing and therefore an efficient method is needed to track the changes and seek for the global optima solution simultaneously. Despite the fact that population based methods have proven to be efficient in solving static problems, they are usually suffer from the diversification when dealing with the dynamic problems [1]. The main reason for this is that the traditional population based methods usually converge to a single optima [2, 3], whilst in dynamic problems the solution landscape keeps changing which means that there are several local optima points that would be revealed during the solving process. One of the ways to overcome the shortcomings of population based methods is to maintain the population diversity. In consequence, several population diversity mechanisms have been employed within population based methods in order to tackle the dynamic optimisation problems. An example of these approaches is the use of memory mechanism within genetic algorithm to store some