Abstract—Recently, the behavior of natural phenomena has become one the most popular sources for researchers in to design optimization algorithms. One of the recent heuristic optimization algorithms is Magnetic Optimization Algorithm (MOA) which has been inspired by magnetic field theory. It has been shown that this algorithm is useful for solving complex optimization problems. The original version of MOA has been introduced in order to solve the problems with continuous search space, while there are many problems owning discrete search spaces. In this paper, the binary version of MOA named BMOA is proposed. In order to investigate the performance of BMOA, four benchmark functions are employed, and a comparative study with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is provided. The results indicate that BMOA is capable of finding global minima more accurate and faster than PSO and GA.
Index Terms—Optimization algorithm, evolutionary algorithm, genetic algorithm, particle swarm optimization, PSO.
Authors are with the Soft Computing Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia (e-mail: ali.mirjalili@gmail.com; sitizaiton@utm.my).
Cite: SeyedAli Mirjalili and Siti Zaiton Mohd Hashim, "BMOA: Binary Magnetic Optimization Algorithm," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 204-208, 2012.