A Geometric Structure-Based Particle Swarm Optimization Algorithm for Multiobjective Problems

Yuan, Wenqiang; Liu, Yusheng; Wang, Hongwei; Cao, Yuchen

doi:10.1109/tsmc.2016.2523938

Cited by 27 publications

(6 citation statements)

References 26 publications

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“…Metaheuristic approaches are a class of algorithms inspired by natural phenomena, such as the genetic algorithm (GA) [30], ant colony optimization (ACO) [31] and PSO [32]. Metaheuristic approaches utilize iterative optimization to find near optimal solutions, which are widely applied in solving intractable optimization problems [33], such as multiobjective optimization [34] and bearing fault detection [35].…”

Section: B Metaheuristic Approaches For Vnementioning

confidence: 99%

Distributed Virtual Network Embedding System With Historical Archives and Set-Based Particle Swarm Optimization

Song

Chen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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Virtual network embedding (VNE) is an important problem in network virtualization for the flexible sharing of network resources. While most existing studies focus on centralized embedding for VNE, distributed embedding is considered more scalable and suitable for large-scale scenarios, but how virtual resources can be mapped to substrate resources effectively and efficiently remains a challenging issue. In this paper, we devise a distributed VNE system with historical archives (HAs) and metaheuristic approaches. First, we introduce metaheuristic approaches to each delegation of the distributed embedding system as the optimizer for VNE. Compared to the heuristicbased greedy algorithms used in existing distributed embedding approaches, which are prone to be trapped in local optima, metaheuristic approaches can provide better embedding performance for these distributed delegations. Second, an archive-based strategy is also introduced in the distributed embedding system to assist the metaheuristic algorithms. The archives are used to record the up-to-date information of frequently repeated tasks. By utilizing such archives as historical memory, metaheuristic algorithms can further improve embedding performance for frequently repeated tasks. Following this idea, we incorporate the set-based particle swarm optimization (PSO) as the optimizer and propose the distributed VNE system with HAs and setbased PSO (HA-VNE-PSO) system to solve the VNE problem in a distributed way. HA-VNE-PSO is empirically validated in

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Section: B Metaheuristic Approaches For Vnementioning

confidence: 99%

Distributed Virtual Network Embedding System With Historical Archives and Set-Based Particle Swarm Optimization

Song

Chen

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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“…The duty cycle approach is used for scheduling the smallest set of sensor nodes into active mode [28]. In addition to these, other approaches are based on localization [29], geometry [30], and hybridization of direct information methods [31] to solve k-connectivity issues in wireless sensor networks.…”

Section: Related Workmentioning

confidence: 99%

A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

et al. 2021

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A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of p-degrees of connectivity concerning objective functions' evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for p-disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway.

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“…Meanwhile, PSO has attained remarkable attention from researchers over the past decade for its ease of implementation and high efficiency in solving single objective problems, especially in optimizing continuous problems [37][38][39][40]. On the one hand, there are mainly two operations in a classical PSO algorithm as described above, i.e., velocity updating and position updating, and there are only three parameters that need to be adjusted, including the inertia weight w, the learning factor c 1 and c 2 .…”

Section: Motivationsmentioning

confidence: 99%

Self-Regulated Particle Swarm Multi-Task Optimization

Zheng

Zhou

et al. 2021

Sensors

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Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.

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A Geometric Structure-Based Particle Swarm Optimization Algorithm for Multiobjective Problems

Cited by 27 publications

References 26 publications

Distributed Virtual Network Embedding System With Historical Archives and Set-Based Particle Swarm Optimization

Distributed Virtual Network Embedding System With Historical Archives and Set-Based Particle Swarm Optimization

A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

Self-Regulated Particle Swarm Multi-Task Optimization

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