A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

• Published in: Engineering optimization Vol. 48; no. 1; pp. 73 - 93
• Main Authors: Liang, Yun-Chia, Cuevas Juarez, Josue Rodolfo
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.01.2016; Taylor & Francis Ltd
• Subjects: Algorithms; continuous optimization; Engineering; Evolution; Evolutionary algorithms; Genetic algorithms; global optimization; Heuristic methods; metaheuristic; Optimization; Optimization algorithms; Searching; Simulation; Swarm intelligence; virus optimization algorithm; Viruses
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2014.994868

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.