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MESPBO: Multi-Strategy-Enhanced Student Psychology-Based Optimization Algorithm for Global Optimization Problems and Feature Selection Problems. [PDF]
Biomimetics (Basel)Zhai G, Li S.
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Opposition based learning: A literature review
Swarm and Evolutionary Computation, 2018Abstract Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the opposite relationship among entities. In 2005, for the first time the concept of opposition was introduced which has attracted a lot of research efforts in the last decade.
Sedigheh Mahdavi +2 more
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Improved grasshopper optimization algorithm using opposition-based learning
Expert Systems with Applications, 2018Abstract This paper proposes an improved version of the grasshopper optimization algorithm (GOA) based on the opposition-based learning (OBL) strategy called OBLGOA for solving benchmark optimization functions and engineering problems. The proposed OBLGOA algorithm consists of two stages: the first stage generates an initial population and its ...
Ahmed A. Ewees +2 more
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Firefly algorithm with generalised opposition-based learning
International Journal of Wireless and Mobile Computing, 2015Firefly Algorithm FA is a new optimisation algorithm based on swarm intelligence, which has shown good performance on many optimisation problems. However, the standard FA easily falls into local minima because of too many attraction operations. To enhance the performance of the standard FA, a new FA is proposed in this paper.
Hui Wang, Wenjun Wang, Hui Sun
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Chaotic Evolution Algorithms Using Opposition-Based Learning
2019 IEEE Congress on Evolutionary Computation (CEC), 2019We propose a method for accelerating chaotic evolution (CE) search using the triple and quadruple comparison mechanisms. We utilize some performance measurements to analyse and verify our proposed algorithm with benchmark functions. The CE is one of evolutionary computation (EC) algorithms that fuses the iteration of evolution and the ergodicity of a ...
Tianshui Li, Yan Pei
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Opposition based comprehensive learning particle swarm optimization
2008 3rd International Conference on Intelligent System and Knowledge Engineering, 2008This paper proposes a novel scheme that we call the opposition based comprehensive learning particle swarm optimizers (OCLPSO), which employs opposition based learning (OBL) for population initialization and also for exemplar selecting. This scheme enables the swarm to explore and exploit with the more diversity and not to be premature convergence ...
null Zhangjun Wu +3 more
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Rotation-Based Learning: A Novel Extension of Opposition-Based Learning
2014Opposition-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 +5 more
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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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Competitive Swarm Optimization with Dynamic Opposition-based Learning
2018 IEEE International Smart Cities Conference (ISC2), 2018In order to enable the PSO to jump out of the local optima, we propose a Competitive Swarm Optimization with Dynamic Opposition-based learning (CSO-DOL). CSO-DOL contains two strategies: Competitive Learning and Opposition-based Learning. In each iteration, two randomly selected particles compete to get the winner and the loser.
Yangfan Zhang, Jun Sun
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Constrained differential evolution using generalized opposition-based learning
Soft Computing, 2016Differential evolution (DE) is a well-known optimization approach to deal with nonlinear and complex optimization problems. However, many real-world optimization problems are constrained problems that involve equality and inequality constraints. DE with constraint handling techniques, named constrained differential evolution (CDE), can be used to solve
Wenhong Wei +3 more
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