Virus Optimization Algorithm(VOA):a novel metaheuristic for optimization problem

• Main Authors: Josue Rodolfo Cuevas Juarez, 古定華
• Other Authors: Yun-Chia Liang
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ct3ak8

碩士 === 元智大學 === 工業工程與管理學系 === 107 === 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 behavior 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. Additionally, extensions of the algorithm are presented in this dissertation where a self-adaptive and discrete version of VOA are proposed (SaVOA and DVOA, respectively).