A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

Abstract

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called ‘antivirus’) to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Keywords:

• virus optimization algorithm
• metaheuristic
• continuous optimization
• global optimization

Supplemental data

Supplemental data for this article can be accessed at http://dx.doi.org/10.1080/0305215X.2014.994868.

Additional information

Funding

This work was partially supported by the Ministry of Science and Technology, Taiwan [grant NSC100-2628-E-155-004-MY3] and by the Innovation Center for Big Data and Digital Convergence at Yuan Ze University.