Titlebook: Advances in Swarm Intelligence; 5th International Co Ying Tan,Yuhui Shi,Carlos A. Coello Coello Conference proceedings 2014 Springer Intern

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Development on Harmony Search Hyper-heuristic Framework for Examination Timetabling Problem a combination of improvement heuristics which consist of neighborhood structure strategies. The proposed approach is tested using the examination timetabling tracks in Second International Timetabling Competition (ITC-2007) benchmarks. Experimentally, the HSHH approach can achieve comparable results with the comparative methods in the literature.

Mhc-Molecule

熄滅

Parallel Bees Swarm Optimization for Association Rules Mining Using GPU Architecturelutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×70 in most data sets, furthermore, the WebDocs benchmark is handled with less than forty hours.

青少年

A Particle Swarm Optimization Based Pareto Optimal Task Scheduling in Cloud Computingnew variant of continuous Particle Swarm Optimization (PSO) algorithm, named Integer-PSO, is proposed to solve the bi-objective task scheduling problem in cloud which out performs the smallest position value (SPV) rule based PSO technique.

Resistance

Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planningove global best properties and enhance the convergence speed. The comparative simulation results show that our proposed PPPIO algorithm is more efficient than the basic PIO and particle swarm optimization (PSO) in solving UAV three-dimensional path planning problems.

觀察

defile

現(xiàn)實(shí)

Semi-supervised Ant Evolutionary Classificationon is carried out to maintain the history colony information as well as the scale of swarms. Theoretical analysis and experimental results show the effectiveness of our proposed model for evolutionary data classification.

津貼

A Novel Rough Set Reduct Algorithm to Feature Selection Based on Artificial Fish Swarm Algorithm genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO) and chaotic binary particle swarm optimization (CBPSO). Experiments demonstrate that the proposed algorithm could achieve the minimal reduct more efficiently than the other methods.

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