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Enhancing particle swarm optimization using generalized opposition-based learning
Information Sciences, 2011Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome
Hui Wang +4 more
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Shuffled frog-leaping algorithm using elite opposition-based learning
International Journal of Sensor Networks, 2014Shuffled frog-leaping algorithm (SFLA) has been shown that it can yield good performance for solving various optimisation problems. However, it tends to suffer from premature convergent when solving complex problems. This paper presents an effective approach, called SFLA using elite opposition-based learning (EOLSFLA), which employs elite population to
Jia Zhao, Li Lv
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Grey Wolf Optimizer with Crossover and Opposition-Based Learning
2020Grey wolf optimizer (GWO) is a relatively new optimizer in the field of swarm intelligence. It is based on the leadership hierarchy and hunting behavior of grey wolves in nature. Due to less number of parameters and ease of implementation, it has gained significant interest among the researchers of different fields.
Shitu Singh, Jagdish Chand Bansal
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Application of Opposition-Based Reinforcement Learning in Image Segmentation
2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing, 2007In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropriate local values and segment the object. The agent uses an image and its manually segmented version and takes some actions to change the environment (the quality of segmented
Farhang Sahba +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), 2023Junqi Geng, Xinghua Liu
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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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Harmony search based on improved partial opposition-based learning
2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019A great deal of researchers has concentrated on creating powerful systems for bundle questions, and a ton of magnificent methodologies have been proposed. Shockingly, the vast majority of the current strategies center around a little volume of information.
K. Chaitanya +2 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), 2006Opposition-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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A Hybrid Differential Evolution Algorithm with Opposition-based Learning
2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2012Differential evolution (DE) is a popular optimization technique, however it also tends to suffer from premature convergence. One possible way to fix this problem is adaptively to choose the right mutation strategy and control parameter setting for distinct problems.
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