Results 251 to 260 of about 549,517 (292)

Random-opposition-based Learning for Computational Intelligence

2019
In 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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Improved clustering algorithm with adaptive opposition-based learning

2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(, 2017
In 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, 2015
Harmony 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, Shenwen Wang, Xuezhi Yue, Huogen Yang   +3 more
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Rotation-Based Learning: A Novel Extension of Opposition-Based Learning

2014
Opposition-based learning (OBL) scheme is an effective mechanism to enhance soft computing techniques, but it also has some limitations. To extend the OBL scheme, this paper proposes a novel rotation-based learning (RBL) mechanism, in which a rotation number is achieved by applying a specified rotation angle to the original number along a specific ...
Huichao Liu, Zhijian Wu, Huanzhe Li, Hui Wang, Shahryar Rahnamayan, Changshou Deng   +5 more
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Speeded-up cuckoo search using opposition-based learning

2014 14th International Conference on Control, Automation and Systems (ICCAS 2014), 2014
For 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, Yeoun-Jae Kim, Jeong-Jung Kim, Ju-Jang Lee   +3 more
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An opposition-based learning competitive particle swarm optimizer

2016 IEEE Congress on Evolutionary Computation (CEC), 2016
An 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, Wei Fang, Xiaojun Wu, Jun Sun, Shi Cheng   +4 more
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Opposition based learning ingrained shuffled frog-leaping algorithm

Journal of Computational Science, 2017
Abstract Shuffled frog-leaping algorithm (SFLA) is a kind of memetic algorithm. Randomicity and determinacy, the two keywords of SFLA ensures flexibility, robustness and exchange of information effectively in SFLA. In the basic structure of SFLA, the frogs are divided into memeplexes based on their fitness values where they forage for food.
Tarun Kumar Sharma, Millie Pant
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Enhancing firefly algorithm using generalized opposition-based learning

Computing, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu, Shuhao, Zhu, Shenglong, Ma, Yan, Mao, Demei   +3 more
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Partial opposition-based learning using current best candidate solution

2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016
Opposition based learning (OBL) has been gaining significant attention in machine learning, specially, in metaheuristic optimization algorithms to take OBL's advantage for enhancing their performance. In OBL, all variables are changed to their opposites while some variables are currently holding proper values which are discarded and converted to worse ...
Sedigheh Mahdavi, Shahryar Rahnamayan, Kalyanmoy Deb   +2 more
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Opposition-Based Learning Embedded Shuffled Frog-Leaping Algorithm

2017
Shuffled frog-leaping algorithm (SFLA), a memetic algorithm modeled on the foraging behavior of natural species called frogs. SFLA embeds the features of both particle swarm optimization (PSO) and shuffled complex evolution (SCE) algorithm. It is well documented in literature that SFLA is an efficient algorithm to solve non-traditional optimization ...
Tarun Kumar Sharma, Millie Pant
openaire   +1 more source