Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application. 

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Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review

Compared to centralized generation, distributed generations (DGs) have numerous advantages including real power loss reduction, voltage deviation reduction, network stability enhancement, emission reduction, capacity increase of transmission lines and congestion reduction in distribution networks. Optimum placement of DGs plays a crucial role in this regard. In this paper, Firefly Algorithm (FA) was employed for sizing / sitting of various DGs in distribution networks. The aim of this paper was to minimize power loss by taking into account power factor, active power, and reactive power of DGs. Furthermore, different active and/or reactive generating/consuming DGs were also considered. The performance analysis of the proposed method was validated on standard IEEE 33- and 69-bus test systems.

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Optimal Sizing and Sitting of Distributed Generations in Power Distribution Networks Using Firefly Algorithm

Considering present shortage of fossil fuels and discharges of ozone harming substances, power developed from Renewable Energy Sources (RES) is identified as the excellent choice for producing the electricity. The characteristic of an inverter is to transform the dc power into ac power to fulfill out the requirements of load. Despite its advantage, the presence of harmonics in the output voltage reduces both the efficiency and the performance of the inverter. Several researches have been carried out since last three decades for eliminating the harmonics. Based upon several researches, it reveals that the Selective Harmonic Elimination Pulse-width Elimination technique (SHEPWM) has proven to be the best in eliminating lower order harmonics. But when calculus based methods are used for solving the non-linear transcendental equations, this technique has shown some complications. Artificial Intelligence (AI) techniques appear to be better in solving the above said equations. This review paper provides the performance of some AI techniques used for eliminating the harmonics in inverters. Based upon the information collected from various literatures and its results, conclusion has been made.

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Optimization of switching angles for selective harmonic elimination in cascaded h-bridge multilevel inverters employing artificial intelligence techniques – a mini review

Power quality investigation of CHB nine-level converter based large-scale solar PV system with different modulation schemes

Multilevel inverters (MLI) are popular in high-power applications. MLIs are generally configured to have switches reduced by switching techniques that eliminate low-order harmonics. The selective harmonic elimination (SHE) method, which significantly reduces the number of switching, determines the optimal switching moments to obtain the desired output voltage and eliminates the desired harmonic components. To solve the SHE problem, classical methods are primarily employed. The disadvantages of such methods are the high probability of trapping in locally optimal solutions and their dependence on initial controlling parameters. One solution to overcome this problem is the use of metaheuristic algorithms. In this study, firstly, 22 metaheuristic algorithms with different sources of inspiration were used to solve the SHE problem at different levels of MLIs, and their performances were extensively analyzed. To reveal the method that offers the best solution, these algorithms were first applied to an 11-level MLI circuit, and six methods were determined as a result of the performance analysis. As a result of the evaluation, the outstanding methods were SPBO, BMO, GA, GWO, MFO, and SPSA. As a result of the application of superior methods to 7-, 11-, 15-, and 19-level MLIs according to the IEEE 519—2014 standard, it has been shown that BMO outperforms in 7-level MLI, GA in 11-level MLI, and SPBO in 15- and 19-level MLIs in terms of THD, while in terms of output voltage quality, GA in 7-level MLI, BMO in 11-level MLI, GA and SPSA in 15-level MLI, and SPSA in 19-level MLI come forward.

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Investigation of Recent Metaheuristics Based Selective Harmonic Elimination Problem for Different Levels of Multilevel Inverters

This paper presents the selective harmonic elimination of cascade H-bridge multilevel inverters using shuffled frog leaping algorithm. This algorithm takes the advantages of the genetic-based memetic algorithm and the social behavior-based PSO algorithm. In addition, this study provides a new fundamental modulation technique with SHE for multilevel inverters which can generate output waveforms with a full range of modulation indices. There are two control objectives formulated as a multi-objective optimization problem. The mentioned algorithm finds the optimal solution set of switching angles. Simulation is performed in MATLAB to confirm the validity of the proposed method.

Mehrnoosh fakhari

Papers

New fundamental modulation technique with SHE using shuffled frog leaping algorithm for multilevel inverters