Velocity pausing particle swarm optimization: a novel variant for global optimization

Shami, Tareq M.; Mirjalili, Seyedali; Al-Eryani, Yasser F.; Daoudi, Khadija; Izadi, Saadat; Abualigah, Laith

doi:10.1007/s00521-022-08179-0

Cited by 20 publications

(13 citation statements)

References 96 publications

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“…The benefits of GA-PSO, a combination of genetic and particle swarm optimization, outweigh those of employing either approach alone. An improved search process and maybe superior solutions are the results of the hybrid strategy, which merges GA's exploration skills with PSO's exploitation characteristics (Shami et al, 2023 ). In order to effectively explore the search space, GA makes use of its mutation and crossover operators.…”

Section: Problem Statementmentioning

confidence: 99%

“…The generated chromosomes are what is sent into the PSO algorithm after the first half of the set number of iterations has been finished. In the PSO method, chromosomes are referred to as particles, and with each iteration of the PSO algorithm (Shami et al, 2023 ), these particles go through a process that results in a little but noticeable improvement. In order to accurately reflect the answer to the workflow task issue, the particle that has the lowest fitness value has been chosen.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

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Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.

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Section: Problem Statementmentioning

confidence: 99%

Section: The Proposed Algorithmmentioning

confidence: 99%

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Saad,

Enam,

Qureshi

2024

Front. Big Data

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“…In 2023, Tareq M. Shami introduced the concept of velocity pausing into the particle swarm optimization (PSO) algorithm, marking a significant advancement in its performance [40]. This modification allows each particle the option to temporarily suspend velocity updates during iterations, instead selecting from three predefined velocities: slow, fast, and constant.…”

Section: Velocity Pausingmentioning

confidence: 99%

“…To validate the performance of VASPSO, five PSO variants were employed: VPPSO [40], MPSO [42], OldMPSO [43] (to distinguish from the previous MPSO), AGPSO [44], and IPSO [45]; along with seven other swarm intelligence algorithms, including DE [46], GWO [47], CSO [48], DBO [49], BWO [50], SSA [51], and ABC [52]. These algorithms are listed in Table 2.…”

Section: Terminal Replacement Mechanismmentioning

confidence: 99%

Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy

Tang,

Meng

2024

Symmetry

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Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with complex optimization tasks. To address these shortcomings, an enhanced PSO algorithm incorporating velocity pausing and adaptive strategies is proposed. By leveraging the search characteristics of velocity pausing and the terminal replacement mechanism, the problem of premature convergence inherent in standard PSO algorithms is mitigated. The algorithm further refines and controls the search space of the particle swarm through time-varying inertia coefficients, symmetric cooperative swarms concepts, and adaptive strategies, balancing global search and local exploitation. The performance of VASPSO was validated on 29 standard functions from Cec2017, comparing it against five PSO variants and seven swarm intelligence algorithms. Experimental results demonstrate that VASPSO exhibits considerable competitiveness when compared with 12 algorithms. The relevant code can be found on our project homepage.

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“…Additionally, two intervention operations, dispersion and aggregation, are devised to mitigate premature convergence and stagnation problems. A concept of velocity pausing PSO (VPPSO) provides particles with a third movement option, enabling them to retain their previous iteration's velocity [32]. VPPSO aims to enhance explorationexploitation balance and prevent premature convergence by altering the first term of the PSO velocity equation.…”

Section: Introductionmentioning

confidence: 99%

Partition Bound Random Number-Based Particle Swarm Optimization for Analog Circuit Sizing

Shreeharsha,

Siddharth,

Vasantha

et al. 2023

IEEE Access

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This work introduces a Partition Bound Particle Swarm Optimization (PB-PSO) algorithm to enhance convergence rates in analog circuit optimization. Two new parameters, ζ 1 and ζ 2 , are incorporated to adaptively update particle velocities based on iteration numbers. The parameter ζ 1 depends on the nonlinear convergence factor (α) and the number of iterations, N . The results indicate that ζ 1 's optimal value occurs with α = 4. ζ 2 partitions iterations into two regions, aiding local and global search. The PB-PSO algorithm, implemented in Python, demonstrates higher convergence rates than existing methods, with successful designs verified through Cadence-Virtuoso circuit simulations. The proposed PB-PSO algorithm converges in 15 and 13 iterations for differential amplifier and two-stage op-amp respectively. For a case study of two-stage amplifier, it achieves a gain of 60.4 dB with a phase margin of 79.76 • , meeting input specifications within constraints. The figure of merit was then evaluated using the obtained parameters, which turns out to be 0.275 V −2 . INDEX TERMS Particle swarm optimization (PSO), analog circuit sizing, low power, operational amplifiers, constrained optimization.

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Velocity pausing particle swarm optimization: a novel variant for global optimization

Cited by 20 publications

References 96 publications

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application

Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy

Partition Bound Random Number-Based Particle Swarm Optimization for Analog Circuit Sizing

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