Solving Fractional Programming Problems based on Swarm Intelligence

Raouf, Osama Abdel; Hezam, Ibrahim M.

doi:10.1007/s40092-014-0056-8

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…Handoff is a way to handle the dynamic associations of a versatile customer while moving within a remote system [2]. The Vertical Handoff, or Framework Handoff, uses the handoff between different systems [3], [4]. Generally, a handoff look is made on the evaluation of the received signal strength (RSS) in a versatile hub.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Bandwidth Optimization for Vertical Handoff using Hybrid Cuckoo Search and Genetic Algorithm Scheme

Khurana

Jha

Gupta

2022

J. Phys.: Conf. Ser.

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Future remote systems should have the option to handle the gain in a different system state. One of the most of the time tried issues for coordination is the vertical handoff, which is an option to handoff portable hubs between different systems. While customary handoff codes are based on quality checks, vertical handoffs must assess additional factors, for example, funding costs, proposed governance, systematic location, and customer learning. In this paper, some improvements are proposed for the implementation of vertical handoff option calculations aimed at increasing the nature of the administration experienced by each customer. Firstly, talk about the idea of alignment-based handoff is done. At that point, cost efficiency can be achieved by sending decisions on the target systems depending on the classification of the customer and the measurements provided by the system. Finally, a demonstration investigation suggests that the proposed reforms can achieve significant gains like governance and the productive use of assets. In the present work, two other half-schemes are proposed using the cuckoo search calculation and the genealogical calculation. In the two mixing plots, the calculation consists of two steps: the primary stage, which examines the location of the cuckoo search (CS), or the genetic algorithm (GA). In the subsequent stage, to improve worldwide pursuit and dispose of catching into a few neighbourhood optima is done. Reproducibility results and testing with old-time CS and GA calculations confirm the feasibility of the proposed computation in designing various benchmark correction capabilities.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Bandwidth Optimization for Vertical Handoff using Hybrid Cuckoo Search and Genetic Algorithm Scheme

Khurana

Jha

Gupta

2022

J. Phys.: Conf. Ser.

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“…The applicability of the proposed hybrid algorithm in solving ratio functions [10] and application problem is validated in this section. Basically, in this section four types of problems are discussed.…”

Section: Numerical Problemsmentioning

confidence: 99%

An improved Evaporation Rate-Water Cycle Algorithm based Genetic Algorithm for solving generalized ratio problems

Veeramani

Sharanya

2021

RAIRO-Oper. Res.

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This paper presents an efficient metaheuristic approach for optimizing the generalized ratio problems such as the sum and multiplicative of linear or nonlinear ratio objective function with affine constraints. This paper focuses on the significance of hybrid techniques, which are implemented by using GA and ER-WCA to increase efficiency and robustness for solving linear and nonlinear generalized ratio problems. Initially, GA starts with an initial random population and it is processed by genetic operators. ER-WCA will observe and preserve the GAs fittest chromosome in each cycle and every generation. This Genetic ER-WCA algorithm is provided with better optimal solutions while solving constrained ratio optimization problems. Also, the effectiveness of the proposed genetic ER-WCA algorithm is analyzed while solving the large scale ratio problems. The results and performance of the proposed algorithm ensures a strong optimization and improves the exploitative process when compared to the other existing metaheuristic techniques. Numerical problems and applications are used to test the performance of the convergence and the accuracy of the approached method. The behavior of this Genetic ER-WCA algorithm is compared with those of evolutionary algorithms namely Neural Network Algorithm, Grey Wolf Optimization, ER-WCA, Water Cycle Algorithm, Firefly algorithm, Cuckoo search algorithm. The evaluated results show that the proposed algorithm increases the convergence and accuracy more than other existing algorithms.

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“…Parer [38] used values c 1 = c 2 = 2.05, Swarm size P S = 10, and inertia weight w = 0.76 to study the performance of a FOPID controller for robot trajectory control. Control parameters c 1 = 0.12, c 2 = 1.2, P S = 50, and w from 0.4 to 0.9 are used in paper [19] for solving fractional programming problems. Paper [39] carried out a detailed study for parameters selection of PSO algorithm.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…Evolutionary and Swarm Intelligence (SI) algorithms such as Genetic Algorithm (GA) [17], Particle Swarm Optimization (PSO) [18,19], and Artificial Bee Colony Algorithm (ABC) [20] techniques have a long history in solving optimization problems of control systems. In [21] the Imperialist Competitive Algorithm (ICA) is used for robust load-frequency control of power systems.…”

Section: Introductionmentioning

confidence: 99%

Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

Haji

Monje

2017

Applied Soft Computing

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The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The

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Solving Fractional Programming Problems based on Swarm Intelligence

Cited by 11 publications

References 17 publications

RETRACTED: Bandwidth Optimization for Vertical Handoff using Hybrid Cuckoo Search and Genetic Algorithm Scheme

RETRACTED: Bandwidth Optimization for Vertical Handoff using Hybrid Cuckoo Search and Genetic Algorithm Scheme

An improved Evaporation Rate-Water Cycle Algorithm based Genetic Algorithm for solving generalized ratio problems

Fractional order fuzzy-PID control of a combined cycle power plant using Particle Swarm Optimization algorithm with an improved dynamic parameters selection

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