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SYMPHONY: Synergistic Hierarchical Metric-Fusion and Predictive Hybrid Optimization for Network Yield-A VANET Routing Protocol. [PDF]

Sensors (Basel)
Kazi AK, Imran M, Asif R, Hina S.
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Dynamic multi-swarm global particle swarm optimization

2019 IEEE Congress on Evolutionary Computation (CEC), 2019
This paper proposes a dynamic multi-swarm global particle swarm optimization (DMS-GPSO) that consists of two novel strategies to balance the exploration and exploitation abilities. In DMS-GPSO, the entire population is divided into a global sub-swarm and dynamic multiple sub-swarms. During the evolutionary process.
Yichao Tang, Xiong Li, Yinglong Zhang, Xuewen Xia, Ling Gui   +4 more
semanticscholar   +3 more sources

Multi-swarm Optimization in Dynamic Environments

EvoWorkshops, 2004
Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present new variants of Particle Swarm Optimization (PSO) specifically designed to work well in dynamic environments.
T. Blackwell, J. Branke
semanticscholar   +3 more sources

Multi swarm optimization algorithm with adaptive connectivity degree

Applied Intelligence, 2018
Particle swarm optimization algorithms are very sensitive to their population topologies. In all PSO variants, each particle adjusts it flying velocity according to a set of attractor particles. The cardinality of this set is a feature of neighborhood topology.
R. Vafashoar, M. Meybodi
semanticscholar   +2 more sources

Multi-swarm Optimization with Chaotic Mapping for Dynamic Optimization Problems

2015 8th International Symposium on Computational Intelligence and Design (ISCID), 2015
In real-world applications, many optimization problems are dynamic, therefore the goal of optimization algorithms is not only to obtain the optimal solution, but also to have strong adaptive capability to the environment changes and track the trajectory of the optimal solution as closely as possible.
Luyi W. Shen, Lihong Xu, R. Wei, Leilei Cao   +3 more
semanticscholar   +2 more sources

Multi-swarm particle swarm optimization for payment scheduling

2017 Seventh International Conference on Information Science and Technology (ICIST), 2017
The payment scheduling negotiation problem with multi-mode and resource constraints (MRCPSNP) is a practical extension to the resource-constrained project scheduling problem (RCPSP). It considers the interests of both the client and the contractor, who negotiate with each other to maximize their own benefits.
Xiaoming Li, Yingchao Sun, Wei-neng Chen, Jun Zhang   +3 more
semanticscholar   +2 more sources

Multi‐swarm and chaotic whale‐particle swarm optimization algorithm with a selection method based on roulette wheel

Expert Syst. J. Knowl. Eng., 2021
The particle swarm optimization (PSO) and the whale optimization algorithm (WOA) are two admired optimization methods that have drawn various researchers' attention.
K. Asghari, Mohammad Masdari, F. S. Gharehchopogh, R. Saneifard   +3 more
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A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

Transactions of the Institute of Measurement and Control, 2021
In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach.
Jaouher Chrouta, Fethi Farhani, Abderrahmen Zaafouri   +2 more
semanticscholar   +1 more source

Enhanced multi-swarm cooperative particle swarm optimizer

Swarm and Evolutionary Computation, 2022
Abstract In this paper, a novel multi-swarm particle swarm optimizer driven by delayed-activation (DA) strategy and repulsive mechanism, named as enhanced multi-swarm cooperative particle swarm optimizer (EMCPSO) is proposed. EMCPSO is designed to make use of the advantage of multi-swarm technique and overcome the problem of premature convergence of ...
Jiawei Lu, Jian Zhang, Jianan Sheng
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Dynamic multi-swarm particle swarm optimizer

Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005
In this paper, a novel dynamic multi-swarm particle swarm optimizer (PSO) is introduced. Different from the existing multi-swarm PSOs and the local version of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these swarms are regrouped frequently by using various regrouping schedules and ...
J.J. Liang, P.N. Suganthan
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