A Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization for Numerical Multi-objective Problems

2013 ◽  
Vol 8 (4) ◽  
pp. 807-821
Author(s):  
Chenlong Yu ◽  
Yongzai Lu ◽  
Jian Chu
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Extremal Optimization ◽  
Multi Objective

A Multi-Objective Hybrid Algorithm for Optimization of Grid Structures

2018 ◽  
Vol 10 (01) ◽  
pp. 1850009 ◽  
Author(s):  
Zhe Xiong ◽  
Xiao-Hui Li ◽  
Jing-Chang Liang ◽  
Li-Juan Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Pareto Optimality ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Mathematical Functions ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Structural Problems

In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.

scholarly journals A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

2022 ◽  
Vol 8 ◽  
pp. e834
Author(s):  
Sara Mejahed ◽  
M Elshrkawey
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Machine ◽  
Hybrid Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Virtual Machine Placement ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Flower Pollination ◽  
Best Fit

The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) is now critical for the provision of efficient resource management in cloud data centers. The VMP process considers the placement of a set of virtual machines onto a set of physical machines, in accordance with a set of criteria. The optimal solution for multi-objective VMP can be determined by using a fitness function that combines the objectives. This paper proposes a novel model to enhance the performance of the VMP decision-making process. Placement decisions are made based on a fitness function that combines three criteria: placement time, power consumption, and resource wastage. The proposed model aims to satisfy minimum values for the three objectives for placement onto all available physical machines. To optimize the VMP solution, the proposed fitness function was implemented using three optimization algorithms: particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization (FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was tested experimentally. The results of the comparative study between the three algorithms show that the hybrid algorithm has the strongest performance. Moreover, the proposed algorithm was tested against the bin packing best fit strategy. The results show that the proposed algorithm outperforms the best fit strategy in total server utilization.

Job Scheduling in Computational Grid Using a Hybrid Algorithm Based on Particle Swarm Optimization and Extremal Optimization

2018 ◽  
Vol 11 (4) ◽  
pp. 72-86
Author(s):  
Tarun Kumar Ghosh ◽  
Sanjoy Das
Keyword(s):  
Particle Swarm Optimization ◽  
Resource Sharing ◽  
Hybrid Algorithm ◽  
Job Scheduling ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Computational Grid ◽  
Swarm Optimization ◽  
Extremal Optimization ◽  
Comparison Results

Grid computing has been used as a new paradigm for solving large and complex scientific problems using resource sharing mechanism through many distributed administrative domains. One of the most challenging issues in computational Grid is efficient scheduling of jobs, because of distributed heterogeneous nature of resources. In other words, the job scheduling in computational Grid is an NP-hard problem. Thus, the use of meta-heuristic is more appropriate option in obtaining optimal results. In this article, the authors propose a novel hybrid scheduling algorithm which combines intelligently the exploration ability of Particle Swarm Optimization (PSO) with the exploitation ability of Extremal Optimization (EO) which is a recently developed local-search heuristic method. The hybrid PSO-EO reduces the schedule makespan, processing cost, and job failure rate and improves resource utilization. The proposed hybrid algorithm is compared with the standard PSO, population-based EO (PEO) and standard Genetic Algorithm (GA) methods on all these parameters. The comparison results exhibit that the proposed algorithm outperforms other three algorithms.

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