Marine Predators Algorithm: A nature-inspired metaheuristic
Marine Predators Algorithm: A nature-inspired metaheuristic
复制标题
DOI：
10.1016/j.eswa.2020.113377
复制
发表时间：
2020-08-15
影响因子：
8.5
通讯作者：
Gandomi, Amir H.
中科院分区：
文献类型：
--
作者：
Faramarzi, Afshin;Heidarinejad, Mohammad;Gandomi, Amir H.
本文提出了一种自然启发式算法海洋捕食者算法（MPA）及其在工程中的应用。MPA的主要灵感是广泛的觅食策略，即Levy和布朗运动的海洋捕食者沿着与最优相遇率政策的捕食者和猎物之间的生物相互作用。MPA遵循自然规律，在最优觅食策略，并遇到捕食者和猎物之间的海洋生态系统的利率政策。本文评估了MPA在29个测试功能，CEC-BC-2017测试套件，随机生成的景观，三个工程基准以及通风和建筑节能性能领域的两个真实工程设计问题上的性能。MPA与三类现有的优化方法进行了比较，包括（1）GA和PSO作为最充分研究的元算法，（2）GSA，CS和SSA作为几乎最近开发的算法和（3）CMA-ES，SHADE和LSHADE-cnEpSin作为高性能优化器和IEEE CEC竞赛的获奖者。在所有方法中，MPA获得了第二名，与LSHADE-cnEpSin相比，MPA表现出非常有竞争力的结果，是表现最好的方法，也是CEC 2017竞赛的获胜者之一。统计事后分析表明，MPA可以被提名为高性能优化器，是一个明显优于GA，PSO，GSA，CS，SSA和CMA-ES的上级算法，而其性能在统计上与SHADE和LSHADE-cnEpSin相似。源代码可在以下网址公开获取：https：//github.com/afshinfaramarzi/Marine-Predators-Algorithm、http：//built-envi.com/portfolio/marinepredators-algorithm/、https：//www.mathworks.com/matlabcentral/fileexchange/74578-marine-predatorsaltaxm-mpa和http://www.alimirjalili.com/MPA.html。(C)2020爱思唯尔有限公司保留所有权利。
This paper presents a nature-inspired metaheuristic called Marine Predators Algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Levy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems. This paper evaluates the MPA's performance on twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape, three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy performance. MPA is compared with three classes of existing optimization methods, including (1) GA and PSO as the most well-studied metaheuristics, (2) GSA, CS and SSA as almost recently developed algorithms and (3) CMA-ES, SHADE and LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-cnEpSin. The source code is publicly available at: https: //github.com/afshinfaramarzi/Marine-Predators-Algorithm, http: //built-envi.com/portfolio/marinepredators-algorithm/, https://www.mathworks.com/matlabcentral/fileexchange/74578-marine-predatorsalgorithm-mpa, and http://www.alimirjalili.com/MPA.html. (C) 2020 Elsevier Ltd. All rights reserved.