A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Moslem Dehghani, Mokhtar Aly, Jose Rodriguez, Ehsan Sheybani, Giti Javidi
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Abstract

This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms.

一种新颖的自然启发优化算法:灰熊脂肪增加优化器。
本文介绍了一种新的受自然启发的优化算法——灰熊脂肪增加优化器(GBFIO)。GBFIO算法模仿灰熊为准备过冬而积累身体脂肪的自然行为,借鉴了它们的狩猎、捕鱼、吃草、蜂蜜等策略。因此,在GBFIO算法中,建模并考虑了三个数学步骤来解决优化问题:(1)根据过去的经验和嗅觉线索寻找食物来源(如蔬菜、水果、蜂蜜、牡蛎);(二)猎捕动物,保护后代不受捕食者侵害;(3)钓鱼。采用31个标准基准函数和30个CEC2017测试基准函数对GBFIO的性能进行了评价,包括高维单峰、多峰、固定维多峰以及旋转和移位基准函数。此外,还考虑了拉力/压缩弹簧设计、焊接梁设计、压力容器设计和减速器设计等4个约束工程设计问题,验证了所提出的GBFIO算法在求解约束问题方面的有效性。与其他算法相比,GBFIO可以成功地解决各种优化问题,这可以从目标函数的优化结果中看出,特别是在高维目标函数上。此外,还将GBFIO算法的性能与其他常用优化算法的求解能力和效率进行了比较。与其他优化算法相比,GBFIO算法相对于其他已知的优化算法提供了更优或有竞争力的准最优解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
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