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A New Monarch Butterfly Optimization Algorithm with SA Strategy
2019Monarch butterfly optimization (MBO) is a newly proposed meta-heuristic algorithm for solving optimization problems. It has been proved experimentally that MBO is superior to the other five state-of-the-art meta-heuristic algorithms on most test problems.
Xitong Wang, Xin Tian, Yonggang Zhang
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2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2021
In the past decades, various attention has been paid to the global optimization problems. The Monarch Butterfly Optimization (MBO) algorithm is an effective meta-heuristic algorithm for the global optimization problems. However, in the MBO, the diversity of the population is lost in the late iteration. The MBO is easy to trap into the local optima.
Fuqing Zhao +4 more
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In the past decades, various attention has been paid to the global optimization problems. The Monarch Butterfly Optimization (MBO) algorithm is an effective meta-heuristic algorithm for the global optimization problems. However, in the MBO, the diversity of the population is lost in the late iteration. The MBO is easy to trap into the local optima.
Fuqing Zhao +4 more
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2018
After studying the behavior of monarch butterflies in nature, Wang et al. proposed a new promising swarm intelligence algorithm, called monarch butterfly optimization (MBO), for addressing unconstrained optimization tasks. In the basic MBO algorithm, the fixed butterfly adjusting rate is used to carry out the butterfly adjusting operator. In this paper,
Gai-Ge Wang, Guo-Sheng Hao, Zhihua Cui
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After studying the behavior of monarch butterflies in nature, Wang et al. proposed a new promising swarm intelligence algorithm, called monarch butterfly optimization (MBO), for addressing unconstrained optimization tasks. In the basic MBO algorithm, the fixed butterfly adjusting rate is used to carry out the butterfly adjusting operator. In this paper,
Gai-Ge Wang, Guo-Sheng Hao, Zhihua Cui
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Neural Computing and Applications, 2016
The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to ...
Waheed Ali H. M. Ghanem, Aman Jantan
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The aim of the study was to propose a new metaheuristic algorithm that combines parts of the well-known artificial bee colony (ABC) optimization with elements from the recent monarch butterfly optimization (MBO) algorithm. The idea is to improve the balance between the characteristics of exploration and exploitation in those algorithms in order to ...
Waheed Ali H. M. Ghanem, Aman Jantan
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A Monarch Butterfly Optimization Approach to Dynamic Task Scheduling
2020Parallel processing has been employed many years in high-performance computing. Parallel processing is mostly used because it provides concurrency, maximizes load balancing, and minimizes system idle time and execution time which depend on various computer architecture.
Chouhan Kumar Rath +2 more
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Modified and Hybridized Monarch Butterfly Algorithms for Multi-Objective Optimization
2019This paper presents two improved versions of the monarch butterfly optimization algorithm adopted for solving multi-objective optimization problems. Monarch butterfly optimization is a relatively new swarm intelligence metaheuristic that proved to be robust and efficient method when dealing with NP hard problems.
Ivana Strumberger +4 more
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Multiobjective hybrid monarch butterfly optimization for imbalanced disease classification problem
International Journal of Machine Learning and Cybernetics, 2019Datasets obtained from the real world are far from balanced, particularly for disease datasets, since such datasets are usually highly skewed having a few minority classes apart from one or more prominent majority classes. In this research, we put forward the novel hybrid architecture to handle imbalanced binary disease datasets that arrives upon the ...
Nalluri Madhusudana Rao +3 more
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Integrating mutation scheme into monarch butterfly algorithm for global numerical optimization
Neural Computing and Applications, 2018Monarch butterfly optimization algorithm (MBO) has recently been proposed as a robust metaheuristic optimization algorithm for solving numerical global optimization problems. To enhance the performance of MBO algorithm, harmony search (HS) is introduced as a mutation operator during the adjusting operator of MBO.
Mohamed Ghetas, Huah Yong Chan 0001
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Monarch Butterfly Optimization Algorithm for Localization in Wireless Sensor Networks
2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA), 2018In this paper we adjusted recent monarch butterfly optimization algorithm for solving NP-hard wireless sensor networks optimization problem, as one of the most important challenges in this area. The main goal of node localization problem is to find geographical coordinates of each sensor node with unknown position that are randomly deployed in the ...
Marko Beko +2 more
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A Novel Monarch Butterfly Optimization With Greedy Strategy and Self-Adaptive
2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI), 2015Recently, inspired by migration of monarch butterflies in the Northern American, a new kind of metaheuristic algorithm, called monarch butterfly optimization (MBO), is proposed for solving global optimization problems. It has been experimentally shown that MBO outperforms five state-of-the-art metaheuristic algorithms on most benchmarks.
Xinchao Zhao, Gai-Ge Wang, Suash Deb
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