Fitness-Based Acceleration Coefficients to Enhance the Convergence Speed of Novel Binary Particle Swarm Optimization

Mehmood, Yasir; Sadiq, Marium; Shahzad, Waseem; Amin, Faryal

doi:10.1109/fit.2018.00069

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…In this paper, we have proposed a rank-based selfadaptive inertia weight scheme to enhance the performance of NBPSO [2]. In the proposed scheme, an adaptive inertia weight strategy [13,14] is incorporated to enhance the convergence speed. The velocity of each particle is directly controlled by their fitness such that the particle with high fitness gets the high rank and the particle with low fitness gets the lower rank.…”

Section: Computer Science Computational Methods Algorithms and Artificial Intelligence Sessionmentioning

confidence: 99%

“…In [26], the theoretical as well as empirical analysis of effect of inertia weight strategies on the performance of BPSO have been presented. In [14], the value of acceleration coefficients was modified base on the fitness of each particle to improve the convergence speed. Ji et al [28] proposed an effective approach named Improved BPSO to address the formulated problems in feature selection and improve its accuracy.…”

Section: The Binary Pso (Bpso) and Its Variantsmentioning

confidence: 99%

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Rank-Based Self-Adaptive Inertia Weight Scheme to Enhance the Performance of Novel Binary Particle Swarm Optimization

Faryal¹,

Yesir²,

Marium³

et al. 2021

Proceedings of the International Scientific Conference - Sinteza 2021

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Inertia weight is a significant parameter of the particle swarm optimization (PSO) algorithm. Its controllers the search capabilities of PSO and provides a balance between exploration and exploitation. There are a plethora of studies on inertia weight variants of continuous PSO (CPSO). However, a few numbers of studies have been presented for binary PSO (BPSO). In existing BPSO variants, despite different positions of particles, every individual is treated equally by ignoring the dispersion of particles in the search space. To deal with each particle according to its fitness value, we have proposed a Rank-based Self-adaptive Inertia Weight to enhance the performance of the Novel BPSO (NBPSO). The proposed algorithm controls the movement of particles by defining the ranks of each particle based on their fitness. The performance of the proposed algorithm is evaluated on four benchmark test functions. The experimental results show that the proposed method performs better than the compared algorithms in terms of convergence speed.

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Section: Computer Science Computational Methods Algorithms and Artificial Intelligence Sessionmentioning

confidence: 99%

Section: The Binary Pso (Bpso) and Its Variantsmentioning

confidence: 99%

Rank-Based Self-Adaptive Inertia Weight Scheme to Enhance the Performance of Novel Binary Particle Swarm Optimization

Faryal¹,

Yesir²,

Marium³

et al. 2021

Proceedings of the International Scientific Conference - Sinteza 2021

View full text Add to dashboard Cite

show abstract

“…Bu çalışmada SNOPT yazılımı [11] Mehmood vd. [30] ikili Parçacık Sürü Optimizasyonunun hızlanma katsayılarını uygunluk değerine göre bireysel olarak değiştiren yeni bir algoritma önerdiler. Önerilen algoritma yaygın olarak kullanılan dört işlev üzerinde daha iyi yakınsama özelliği gösterdi.…”

Section: Li̇teratür Taramasi (Literature Review)unclassified

Yaygın kullanılan bazı optimizasyon fonksiyonlarının melez bir algoritma ile çözümü

Cengiz

Çomak

2021

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Designing a new hybrid optimization algorithm  Providing a new perspective to achieve optimized results  Designing an optimization algorithm for faster reach optimized solutionsIn this study, the development of a hybrid optimization method has been emphasized in order to eliminate the deficiencies in the structure of heuristic and numerical methods. In our applications, Particle Swarm Optimization (PSO) method and Broydon-Fletcher-Goldfarb-Shanno (BFGS) method are aimed to reach the optimum solution more accurately and faster. In the algorithm of the developed optimization method, the solution is firstly sought with BFGS. Optimum result points are screened using PSO. Until the final result is reached, the intermediate solution points are continuously transferred between the BFGS and the PSO. Using the original BFGS and the original PSO, two different methods are proposed: BFGS first, then PSO hybrid method, and vice versa hybrid method (first PSO then BFGS). Figure A. Recycled aggregate productionPurpose: Although numerical optimization algorithms guarantee the exact solution, they remain relatively slow. On the other hand, heuristic optimization algorithms are fast but they cannot guarantee the exact solution.From this point of view, it is aimed to design an algorithm that achieves both fast and exact solutions.Theory and Methods: In this study, it is proposed that numerical and heuristic methods can be hybridized and exact solutions can be reached faster. In the literature, hybrid optimization algorithms apply heuristic and then numerical approaches. In the developed method, the opposite way was followed. Results:The developed method was tested on the functions used in the literature for testing purposes. Accordingly, as the test function becomes more complex, the speed of the solution is slow in milliseconds according to the comparison algorithm. Conclusion:Consequently, the combination of numerical methods such as BFGS and heuristic methods such as PSO may be an appropriate approach for solution accuracy. As intended in this study, heuristics can be hybridized with other numerical methods. On the other hand, the literature research and the proposed algorithm proved that hybrid algorithms obtained better results than standard heuristics and numerical methods.

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“…Utilizing the sigmoid transfer function, the author in [154] proposed a new binary PSO version where the PSO acceleration coefficients are modified based on the fitness of each particle. The effectiveness of the proposed approach was tested on four problems in the continuous search space and its performance in optimizing binary problems is not validated.…”

Section: Kennedy and Eberhart Introduced The Binary Version Of Pso (B...mentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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Fitness-Based Acceleration Coefficients to Enhance the Convergence Speed of Novel Binary Particle Swarm Optimization

Cited by 9 publications

References 15 publications

Rank-Based Self-Adaptive Inertia Weight Scheme to Enhance the Performance of Novel Binary Particle Swarm Optimization

Rank-Based Self-Adaptive Inertia Weight Scheme to Enhance the Performance of Novel Binary Particle Swarm Optimization

Yaygın kullanılan bazı optimizasyon fonksiyonlarının melez bir algoritma ile çözümü

Particle Swarm Optimization: A Comprehensive Survey

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