Results 301 to 310 of about 550,063 (331)

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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Improving comprehensive learning particle swarm optimiser using generalised opposition-based learning

International Journal of Modelling, Identification and Control, 2011
In this paper, we present an improved comprehensive learning particle swarm optimiser (CLPSO) by using a generalised opposition-based learning concept (GOBL). The proposed approach, called GOCLPSO, employs similar schemes of opposition-based differential evolution (ODE) for opposition-based population initialisation and generation jumping with GOBL ...
Wenjun Wang, Hui Wang, Shahryar Rahnamayan   +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
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Decomposition Based Multi-objective Genetic Algorithm (DMOGA) with Opposition Based Learning

2012 Fourth International Conference on Computational Intelligence and Communication Networks, 2012
Multi-objective evolutionary algorithm has two goals i.e. diversity and convergence while solving MOP (Multi Objective Problem). These two goals can be achieved by proper selection of solutions. Real difficulty is selection of solution in presence of multiple conflicting objectives.
Rahila Patel, M.M. Raghuwanshi, L.G. Malik   +2 more
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Hybrid opposite-based learning marine predator algorithm

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023
Junqi Geng, Xinghua Liu
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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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Enhancing sine cosine algorithm based on social learning and elite opposition-based learning

Computing
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lei Chen, Linyun Ma, Lvjie Li
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Firefly Algorithm with Opposition-Based Learning

2022
Yanping Qiao, Feng Li, Cong Zhang, Xiaofeng Li, Zhigang Zhou, Tao Zhang, Quanhua Zhu   +6 more
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Opposition-Based Learning: A New Scheme for Machine Intelligence

International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2006
Opposition-based learning as a new scheme for machine intelligence is introduced. Estimates and counter-estimates, weights and opposite weights, and actions versus counter-actions are the foundation of this new approach. Examples are provided. Possibilities for extensions of existing learning algorithms are discussed.
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