Mushroom Reproduction Optimization (MRO): A Novel Nature-Inspired Evolutionary Algorithm

被引：17

作者：

Bidar, Mahdi ^{[1
]}

Kanan, Hamidreza Rashidy ^{[2
]}

Mouhoub, Malek ^{[1
]}

Sadaoui, Samira ^{[1
]}

机构：

[1] Univ Regina, Dept Comp Sci, Regina, SK, Canada

[2] Shahid Rajaee Teacher Training Univ, Dept Comp Engn, Tehran, Iran

来源：

2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2018年

关键词：

D O I：

10.1109/CEC.2018.8477837

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a new nature-inspired optimization algorithm namely Mushroom Reproduction Optimization (MRO) inspired and motivated by the reproduction and growth mechanisms of mushrooms in nature. MRO follows the process of discovering rich areas (containing good living conditions) by spores to grow and develop their own colonies. We thoroughly assess MRO performance based on numerous unimodal and multimodal benchmark functions as well as engineering problem instances. Moreover, to further investigate on the performance of the proposed MRO algorithm, we conduct a useful statistical evaluation and comparison with well known meta-heuristic algorithms. The experimental results confirm the high performance of MRO in dealing with complex optimization problems by discovering solutions with better quality.

引用

页码：1762 / 1771

页数：10

共 22 条

[1]

Abbasian R., 2011, P 24 INT FLOR ART IN, P3

[2]

Adorio EP, 2005, MVF-multivariate test functions library in C for unconstrained global optimization

[3] A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems [J].