Data Clustering Based on Particle Swarm Optimization with Neighborhood Search and Cauchy Mutation

2014 ◽  
pp. 151-159 ◽  
Author(s):  
Dang Cong Tran ◽  
Zhijian Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Data Clustering ◽  
Particle Swarm ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Cauchy Mutation

scholarly journals A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Guang Peng ◽  
Yangwang Fang ◽  
Shaohua Chen ◽  
Weishi Peng ◽  
Dandan Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Time Window ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Neighborhood Search ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Cauchy Mutation ◽  
Air Combat

A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.

scholarly journals Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

2020 ◽  
Vol 13 (6) ◽  
pp. 42-51
Author(s):  
Archana Kollu ◽  
◽  
Sucharita Vadlamudi ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Energy Management ◽  
Optimization Technique ◽  
Service Level Agreement ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cauchy Mutation ◽  
Eagle Strategy

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

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