ARO: A new model free optimization algorithm for real time applications inspired by the asexual reproduction

作者：Mansouri Taha; Farasat Alireza^*; Menhaj Mohammad B; Moghadam Mohammad Reza Sadeghi

来源：Expert Systems with Applications, 2011, 38(5): 4866-4874.

DOI：10.1016/j.eswa.2010.09.084

• Model free optimization
• Asexual reproduction
• Bio-inspired algorithms
• Real time optimization
• Genetic algorithm

This paper presents a new individual based optimization algorithm, which is inspired from asexual reproduction known as a remarkable biological phenomenon, called as asexual reproduction optimization (ARO). ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. ARO adaptive search ability along with its strength and weakness points are fully described in the paper. Furthermore, the ARO convergence to the global optimum is mathematically analyzed. To approve the effectiveness of the ARO performance, it is tested with several benchmark functions frequently used in the area of optimization. Finally, the ARO performance is statistically compared with that of an improved genetic algorithm (GA). Results of simulation illustrate that ARO remarkably outperforms GA.

• 出版日期2011-5