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CFO Algorithm Using Niche and Opposition-Based Learning
2018 14th International Conference on Computational Intelligence and Security (CIS), 2018The Central Force Optimization (CFO) algorithm is a new multi-dimensional search-determined heuristic optimization algorithm. The results obtained by using CFO algorithm are unstable and easy to fall into local optimum. To solve this shortcoming, we propose a new algorithm for central gravity optimization using Niche and Opposition-Based Learning ...
Min Li, Fei Liang, Jie Liu
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Artificial Bee Colony Using Opposition-Based Learning
2015To overcome the drawbacks of artificial bee colony(ABC) algorithm that converges slowly in the process of searching and easily suffers from premature, this paper presents an effective approach, called ABC using opposition-based learning(OBL-ABC). It generates opposite solution by the employed bee and onlooker bee, and chooses the better solution as the
Jia Zhao, Li Lv, Hui Sun
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Random-opposition-based Learning for Computational Intelligence
2019In this paper, random-opposition-based learning (ROBL) is proposed. ROBL is a generalized version of opposition-based learning (OBL). ROBL introduces randomness in OBL. ROBL is applied for some metaheuristics and artificial neural network. The examples are provided with preliminary results.
Divya Bairathi, Dinesh Gopalani
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Opposition learning based Harris hawks optimizer for data clustering
Journal of Ambient Intelligence and Humanized Computing, 2021Data clustering is a crucial machine learning technique that helps divide a given dataset into many similar data objects where the data members resemble each other. It is an unsupervised learning algorithm and is hugely applied in different machine learning and data mining applications. k-means algorithm is one of the popular methods for clustering the
Tribhuvan Singh +3 more
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Improved clustering algorithm with adaptive opposition-based learning
2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(, 2017In recent years, clustering has become a hotspot in the field of data mining, as one of the key technologies of getting data distribution and observing the characteristics of class. However, some clustering algorithms depend on the selection of initial clustering centers, and the clustering results easily fall into local optimal.
Qianqian Meng, Lijuan Zhou
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Global harmony search with generalized opposition-based learning
Soft Computing, 2015Harmony search (HS) has shown promising performance in a wide range of real-world applications. However, in many cases, the basic HS exhibits strong exploration ability but weak exploitation capability. In order to enhance the exploitation capability of the basic HS, this paper presents an improved global harmony search with generalized opposition ...
Zhaolu Guo +3 more
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Improved Grey Wolf Optimizer Based on Opposition-Based Learning
2018Swarm intelligence (SI)-based algorithms are very popular optimization techniques to deal with complex and nonlinear optimization problems. Grey wolf optimizer (GWO) is one of the newest and efficient algorithms based on hunting activity and leadership hierarchy of grey wolves.
Shubham Gupta, Kusum Deep
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Speeded-up cuckoo search using opposition-based learning
2014 14th International Conference on Control, Automation and Systems (ICCAS 2014), 2014For several decades, swarm intelligence (SI), emergent collective intelligence of groups of simple agents, has been applied to diverse research areas including optimization problems. Particle swarm optimization, ant colony optimization, artificial bee colony algorithm are well-known examples, and many variants are proposed so far.
So-Youn Park +3 more
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An opposition-based learning competitive particle swarm optimizer
2016 IEEE Congress on Evolutionary Computation (CEC), 2016An opposition-based learning competitive particle swarm optimizer (OBL-CPSO) is proposed to address the problem of premature convergence in PSO. Two learning mechanisms have been employed in OBL-CPSO, which are competitive learning from competitive swarm optimizer (CSO) and opposition-based learning.
Jianhong Zhou +4 more
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Opposition learning based phases in artificial bee colony
International Journal of System Assurance Engineering and Management, 2016Artificial bee colony (ABC) is a recently introduced swarm intelligence algorithm (SIA). Initially only unconstrained problems were handled by ABC, which was later modified by embedding one more parameter called modified rate to handle constrained problems.
Tarun Kumar Sharma, Preeti Gupta
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