Results 241 to 250 of about 549,517 (292)
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Opposition-based learning grey wolf optimizer for global optimization
Knowledge-Based Systems, 2021Abstract Grey wolf optimizer is a novel swarm intelligent algorithm. It has received lots of interest from the heuristic algorithm community for its superior optimization capacity and few parameters. However, it is also easy to trap into the local optimum when solving complex and multimodal functions.
Xiaobing Yu
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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 ...
Hui Wang, Shahryar Rahnamayan
exaly +2 more sources
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 ...
Hui Wang, Shahryar Rahnamayan
exaly +2 more sources
Fast random opposition-based learning Golden Jackal Optimization algorithm
Knowledge-Based Systems, 2023Prabhujit Mohapatra
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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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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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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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