I/F-Race tuned firefly algorithm and particle swarm optimization for K-medoids-based clustering

Khennak, Ilyes; Drias, Habiba; Drias, Yassine; Bendakir, Faysal; Hamdi, Samy

doi:10.1007/s12065-022-00794-z

Cited by 11 publications

(4 citation statements)

References 49 publications

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“…PSO holds a stochastic optimization strategy with population-based and is developed by being socially inspired by swarm behavior among the population. PSO explores a global minimum 𝑎 ∈ ℝ 𝑛 (i.e., the solution with the proper fitness value) by iteratively modifying the velocity and position of each particle in the search space with respect to a certain objective function 𝑓: ℝ 𝑛 → ℝ. PSO utilizes both the best position in the current neighborhood 𝑔 and the particle's best position 𝑝 at iteration 𝑡 (Baygin et al, 2019;Khennak et al, 2023). There exist various extensions proposed for the algorithm (Asif et al, 2022;Mezura-Montes & Coello Coello, 2011;Pedersen, 2010).…”

Section: Pso Revisitedmentioning

confidence: 99%

Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi

Aydin

2023

JCS

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Meta-heuristics are high-level approaches developed to discover a heuristic that provides a reasonable solution to many varieties of optimization problems. The classification problems contain a sort of optimization problem. Simply, the objective herein is to reduce the number of misclassified instances. In this paper, the question of whether meta-heuristic methods can be used to construct linear models or not is answered. To this end, Particle Swarm Optimization (PSO) has been engaged to address linear classification problems. The Particle Swarm Classifier (PSC) with a certain objective function has been compared with Support Vector Machine (SVM), Perceptron Learning Rule (PLR), and Logistic Regression (LR) applied to fifteen data sets. The experimental results point out that PSC can compete with the other classifiers, and it turns out to be superior to other classifiers for some binary classification problems. Furthermore, the average classification accuracies of PSC, SVM, LR, and PLR are 80.8%, 80.6%, 80.9%, and 57.7%, respectively. In order to enhance the classification performance of PSC, more advanced objective functions can be developed. Further, the classification accuracy can be boosted more by constructing tighter constraints via another meta-heuristic.

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Section: Pso Revisitedmentioning

confidence: 99%

Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi

Aydin

2023

JCS

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“…It can be used to evaluate the effectiveness of resource allocation and take corresponding adjustment measures based on the evaluation results, continuously improving the efficiency and optimization results of educational resource allocation. The model can more effectively allocate and utilize various resources, and the total resource allocation varies compared to the benchmark results [9] .…”

Section: Data Social Factorsmentioning

confidence: 99%

Optimal Allocation of Resources for Innovation and Entrepreneurship Education in Universities Based on PSO Algorithm

Ye,

Cheok

2023

Frontiers in Artificial Intelligence and Applications

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Innovation and entrepreneurship (IAE) education in universities is an important way to promote IAE among college students. In order to improve the quality of IAE education in universities, it is necessary to optimize the allocation of its resources. Through the analysis of IAE education resources in colleges and universities, this paper combined PSO (Particle Swarm Optimization) to optimize the allocation of resources, and achieved certain results in practical applications. This paper studied the resource allocation model, defined the optimization objective function, and used PSO for optimization calculation. Experimental data showed that when the number of iterations was 100, it took the longest time, but the optimization effect was the best. The optimization scheme of PSO can effectively improve the effect of IAE practice in colleges and universities, thus promoting the cultivation of students’ innovation spirit and entrepreneurial ability, and providing an effective optimization idea for colleges and universities.

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“…The number of scenarios generated is too large, and there are a large number of similar scenarios; so, it is necessary to reduce the scenarios to obtain typical scenarios, so that one scenario can simulate the effect of multiple scenarios. At present, the rapid forward selection method, the synchronous back generation reduction method, and the scenario number construction method are mainly used for scenario reduction, but these methods have large computational quantities and are complex; so, this paper adopts the K-medoids method [30][31][32] to reduce the scenarios. The core idea of the K-medoids method is to divide the generated scenarios into several groups according to the degree of similarity and to choose one scenario in each group as a typical scenario.…”

Section: Scenario Reduction Based On K-medoidsmentioning

confidence: 99%

Optimal Scheduling of Virtual Power Plant Considering Revenue Risk with High-Proportion Renewable Energy Penetration

Zhang,

Zhao,

et al. 2023

Electronics

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Distributed power supplies have gradually become a new trend in power supply development, but access to a large number of distributed energy sources has a certain impact on the stable operation of the power grid. A virtual power plant (VPP) can integrate a variety of distributed power sources for coordination and optimization; thus, it can effectively solve the difficulties faced by a distributed energy grid connection and promote the complementarity of energy sources. However, renewable energy often has a degree of volatility and randomness when distributed, which can bring certain risks to the operation of the VPP. In order to consider the risks brought by renewable energy, an optimal scheduling model of the VPP, based on an improved generative adversarial network (GAN) and the conditional value at risk (CVaR), was proposed to measure the relationship between the benefits and risks. Firstly, the uncertainty of new energy is analyzed, and wind power and photovoltaic scenarios are generated by the improved GAN; then, typical scenarios are generated by the k-medoids method. Finally, based on the CVaR, the optimal scheduling model of the VPP is established to study the effect of risk weight on VPP revenue. The results show that the model can effectively measure the relationship between the benefits and risks and can provide some references for the VPP to make reasonable operational decisions with different risk preferences.

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I/F-Race tuned firefly algorithm and particle swarm optimization for K-medoids-based clustering

Cited by 11 publications

References 49 publications

Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi

Parçacık Sürü Optimizasyonu Yoluyla Geliştirilen Doğrusal Bir Sınıflandırıcının Analizi

Optimal Allocation of Resources for Innovation and Entrepreneurship Education in Universities Based on PSO Algorithm

Optimal Scheduling of Virtual Power Plant Considering Revenue Risk with High-Proportion Renewable Energy Penetration

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