Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

Bashath, Samar; Ismail, Amelia Ritahani

doi:10.11591/ijeecs.v11.i1.pp300-307

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…Particle swarm optimization is a simple method introduced by Kennedy and Eberhart in 1995 based on the swarm of bird communication inspiration. PSO consider one of the most efficient optimization algorithm used to solve optimization problems [13]- [15]. Because the simplicity and robust performance, PSO attract researcher and engineer [16].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A particle swarm optimization levy flight algorithm for imputation of missing creatinine dataset

Ismail

Aziz

Ralib

et al. 2021

Int. J. Adv. Intell. Informatics

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Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient Acute Kidney Injury (AKI) and those at high risk of developing AKI could be identified. This paper proposes an improved mechanism to machine learning imputation algorithms by introducing the Particle Swarm Levy Flight algorithm. We improve the algorithms by modifying the Particle Swarm Optimization Algorithm (PSO), by enhancing the algorithm with levy flight (PSOLF). The creatinine dataset that we collected, including AKI diagnosis and staging, mortality at hospital discharge, and renal recovery, are tested and compared with other machine learning algorithms such as Genetic Algorithm and traditional PSO. The proposed algorithms' performances are validated with a statistical significance test. The results show that SVMPSOLF has better performance than the other method. This research could be useful as an important tool of prognostic capabilities for determining which patients are likely to suffer from AKI, potentially allowing clinicians to intervene before kidney damage manifests.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A particle swarm optimization levy flight algorithm for imputation of missing creatinine dataset

Ismail

Aziz

Ralib

et al. 2021

Int. J. Adv. Intell. Informatics

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“…During flight, each particle adjusts its trajectory towards its own previous best position (This value is called Pbest), and towards the best previous position attained by any member of its neighborhood or globally, the whole swarm (This value is called Gbest), [11][12][13][14][15][16][17]. The two equations which are used in PSO are velocity update equation 1and position update equations (2).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Impact of inertia weight strategies in particle swarm optimization for solving economic dispatch problem

Meziane¹,

Mouloudi²,

Bouchiba³

et al. 2019

IJEECS

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<p>Particle Swarm Optimization (PSO) is a population based stochastic optimization technique inspired by the social learning of birds or fish. Some of the appealing facts of PSO are its convenience, simplicity and easiness of implementation requiring but few parameters adjustments. Inertia Weight (ω) is one of the essential parameters in PSO, which often significantly the affects convergence and the balance between the exploration and exploitation characteristics of PSO. Since the adoption of this parameter, there have been large proposals for determining the value of Inertia Weight Strategy. In order to show the efficiency of this parameter in the Economic Dispatch problem(ED), this paper presents a comprehensive review of one or more than one recent and popular inertia weight strategies reported in the related literature. Among this five recent inertia weight four were randomly chosen for application and subject to empirical studies in this research, namely, Constant (ω), Random (ω), Global-Local Best (ω), Linearly Decreasing (ω), which are then compared in term of performance within the confines of the discussed optimization problem. Morever, the results are compared to those reported in the recent literature and data from SONELGAZ. The study results are quite encouraging showing the good applicability of PSO with adaptive inertia weight for solving economic dispatch problem.</p>

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“…From a practical point of view, optimization consists of adjusting or fine-tuning system design parameters based on one or more performing functions. Unfortunately, this task is not trivial in many cases, especially when the estimated searching space is complex, nonlinear, discontinuous, or presents high dimensionality [4], [5], [6], [7]. Indeed, most real-world engineering problems present high design complexity, nonlinear constraints, and vast solution domains.…”

Section: Introductionmentioning

confidence: 99%

Automatic Design of Metaheuristics for Practical Engineering Applications

et al. 2023

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It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three wellknown algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems.

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Comparison of Swarm Intelligence Algorithms for High Dimensional Optimization Problem

Cited by 9 publications

References 19 publications

A particle swarm optimization levy flight algorithm for imputation of missing creatinine dataset

A particle swarm optimization levy flight algorithm for imputation of missing creatinine dataset

Impact of inertia weight strategies in particle swarm optimization for solving economic dispatch problem

Automatic Design of Metaheuristics for Practical Engineering Applications

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