Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization

2021 ◽  
Author(s):  
Xinmin Tao ◽  
Xiangke Li ◽  
Wei Chen ◽  
Tian Liang ◽  
Yetong Li ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Particle Swarm ◽  
Hybrid Learning ◽  
Swarm Optimization ◽  
Self Adaptive

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud

2012 ◽  
pp. 88-107 ◽  
Author(s):  
R. Jeyarani ◽  
N. Nagaveni ◽  
R. Vasanth Ram
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Resource Dynamics ◽  
Virtual Machine Provisioning ◽  
Dynamic Resource ◽  
Self Adaptive

Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.

scholarly journals Multi-robot path planning using an improved self-adaptive particle swarm optimization

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142093615
Author(s):  
Biwei Tang ◽  
Kui Xiang ◽  
Muye Pang ◽  
Zhu Zhanxia
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Evolutionary Game ◽  
Particle Swarm ◽  
Computation Time ◽  
Robot Path Planning ◽  
Swarm Optimization ◽  
Robot Path ◽  
Self Adaptive ◽  
Multi Robot

Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.

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