Journal ArticleDOI
Monarch butterfly optimization
TLDR
A comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out, and the results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms.Abstract:
In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub (
https://github.com/ggw0122/Monarch-Butterfly-Optimization
, C++/MATLAB) and MATLAB Central (
http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization
, MATLAB).read more
Citations
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Journal ArticleDOI
Butterfly optimization algorithm: a novel approach for global optimization
Sankalap Arora,Satvir Singh +1 more
TL;DR: A new nature-inspired algorithm, namely butterfly optimization algorithm (BOA) that mimics food search and mating behavior of butterflies, to solve global optimization problems and results indicate that the proposed BOA is more efficient than other metaheuristic algorithms.
Journal ArticleDOI
Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems
TL;DR: Inspired by the phototaxis and Lévy flights of the moths, a new kind of metaheuristic algorithm, called moth search (MS) algorithm, is developed in the present work and significantly outperforms five other methods on most test functions and engineering cases.
Proceedings ArticleDOI
Elephant Herding Optimization
TL;DR: A new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks, inspired by the herding behavior of elephant group.
Journal ArticleDOI
Fuzzy best-worst multi-criteria decision-making method and its applications
Sen Guo,Haoran Zhao +1 more
TL;DR: The results indicate the proposed fuzzy BWM can not only obtain reasonable preference ranking for alternatives but also has higher comparison consistency than the BWM.
Journal ArticleDOI
Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts
TL;DR: This open-source population-based optimization technique called Hunger Games Search is designed to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity.
References
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Grey Wolf Optimizer
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