Fuzzy Evolutionary Hybrid Metaheuristic for Network Topology Design

Youssef, Habib; Sait, Sadiq M.; Khan, Salman A.

doi:10.1007/3-540-44719-9_28

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…Stochastic evolution (StocE) [36] is a non-deterministic iterative algorithm inspired by the behavior of biological processes. In contrast to many other iterative algorithms that operate on a population of solutions, there are only a few evolutionary algorithms, such as StocE [36] and Simulated Evolution [37][38][39], that maintain a single solution throughout their execution. The single solution inStocE is perturbed iteratively to improve the quality of the solution, thus leading to an optimal or quasi-optimal solution.…”

Section: Proposed Genetic Algorithm With Re-warding Mechanismmentioning

confidence: 99%

GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

Mohandes,

Khan,

Rehman

et al. 2023

FME Transactions

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Wind energy has emerged as a potential alternative to traditional energy sources for economical and clean power generation. One important aspect of wind energy generation is the layout design of the wind farm so as to harness maximum energy. Due to its inherent computational complexity, the wind farm layout design problem has traditionally been solved using nature-inspired algorithms. An important issue in nature-inspired algorithms is the termination condition, which governs the execution time of the algorithm. To optimize the execution time, appropriate termination conditions should be employed. This study proposes the concept of a rewarding mechanism to achieve optimization in termination conditions while maintaining the solution quality. The proposed rewarding mechanism, adopted from the stochastic evolution algorithm, is incorporated into a genetic algorithm. The proposed genetic algorithm with the rewarding mechanism (GARM) is empirically tested using real data from a potential wind farm site with different rewarding iterations.

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Section: Proposed Genetic Algorithm With Re-warding Mechanismmentioning

confidence: 99%

GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

Mohandes,

Khan,

Rehman

et al. 2023

FME Transactions

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“…Inspired by the theory of evolution, the algorithm has several distinctive features as mentioned in Section 1. Despite its distinctive features and excellent performance, the algorithm and its hybrid variants have received little attention from researchers in some domains such as healthcare [31], internet traffic engineering [18], network design optimization [6,32], microelectronics [33][34][35], and cloud computing [17,36,37].…”

Section: Basic Simulated Evolution Algorithmmentioning

confidence: 99%

Adaptation of the simulated evolution algorithm for wind farm layout optimization

Khan¹

2022

FME Transactions

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Wind energy is a potential replacement for traditional, fossil-fuel-based power generation sources. One important factor in the process of wind energy generation is to design of the optimal layout of a wind farm to harness maximum energy. This layout optimization is a complex, NP-hard optimization problem. Due to the sheer complexity of this layout design, intelligent algorithms, such as the ones from the domain of natural computing, are required. One such effective algorithm is the simulated evolution (SE) algorithm. This paper presents a simulated evolution algorithm engineered to solve the wind farm layout design (WFLD)optimization problem. In contrast to many non-deterministic algorithms, such as genetic algorithms and particle swarm optimization which operate on a population, the SE algorithm operates on a single solution, decreasing the computational time. Furthermore, the SE algorithm has only one parameter to tune as opposed to many algorithms that require tuning multiple parameters. A preliminary empirical study is done using data collected from a potential location in the northern region of Saudi Arabia. Experiments are carried out on a 10 × 10 grid with 15 and 20 turbines while considering turbines with a rated capacity of 1.5 MW. Results indicate that a simulated evolution algorithm is a viable option for the said problem.

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“…Weight factor is a constant value between 0 and 1. The recommended value for is between 0.5 and 0.8 [25]. Therefore, the value that is used in this study is 0.6.…”

Section: The Inference Module the Inference Module Is Usedmentioning

confidence: 99%

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

Huang

Por

Ang

et al. 2019

Advances in Fuzzy Systems

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Link quality estimation is essential for improving the performance of a routing protocol in a wireless sensor network. Many methods have been proposed to increase the performance of the link quality estimation; however, most of them are not able to evaluate link quality accurately. In this study, a method that uses fuzzy logic to combine both hardware-based and software-based metrics is proposed to improve the accuracy rate for evaluating a link quality. This proposed method consists of three types of modules, the Fuzzifier module, the Inference module, and the Defuzzifier module. The Fuzzifier module is used to determine the degree to which input link quality metrics belong to each fuzzy set through proposed membership functions. The Inference module obtains the rule outputs based on the proposed fuzzy rules and the given inputs acquired from the Fuzzifier module. The Defuzzifier module is used to aggregate the rule outputs inferred from the Inference module. The result from the Defuzzifier module is then used to evaluate the link quality. A simulation conducted to compare the accuracy rates of the proposed method and those found in related works showed that the proposed method had higher accuracy rates for evaluating a link quality.

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Fuzzy Evolutionary Hybrid Metaheuristic for Network Topology Design

Cited by 9 publications

References 12 publications

GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

GARM: A stochastic evolution based genetic algorithm with rewarding mechanism for wind farm layout optimization

Adaptation of the simulated evolution algorithm for wind farm layout optimization

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

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