A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration

Niknam, Taher; Azad‐Farsani, Ehsan

doi:10.1016/j.engappai.2010.02.005

Cited by 99 publications

(36 citation statements)

References 16 publications

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“…• Power system optimization (Rameshkhah et al 2010a(Rameshkhah et al , b, 2011Nejad et al 2010a;Gómez-González and Jurado 2011;Narimani 2011;Sedighizadeh et al 2011;Payam et al 2011;Bijami et al 2011;Jahani et al 2010, Jahani et al 2011aJalilzadeh et al 2011;Ebrahimi et al 2011;Yammani et al 2012;Malekpour et al 2012;Niknam and Farsani 2010;Nejad et al 2010b;Niknam et al 2011aNiknam et al , b, c, 2012bKhorsandi et al 2011;Roy and Chakrabarti 2011).…”

Section: Discussionmentioning

confidence: 95%

Frog Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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Section: Discussionmentioning

confidence: 95%

Frog Inspired Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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“…To enhance local exploration and performance of SFLA, and avoid premature convergence for solving the distribution feeder reconfiguration problem, Niknam and Farsani (2010) developed a new HSFLA. The proposed algorithm named SAPSP-MSFLA uses self-adaptive PSO (using two new turning parameters and increase of other variables' dimensions) and MSFLA (using new frog leaping equation to expand the length and trend of each frog's jump).…”

Section: Hybrid Sflamentioning

confidence: 99%

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

2014

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Shuffled frog leaping algorithm (SFLA) is a meta-heuristic to handle different large-scale optimization problems. SFLA is a population-based algorithm that combines the advantages of memetic algorithm and particle swarm optimization. This paper compares previous researches on SFLA and its effectiveness, with the most applied optimization algorithms reviewed and analyzed. Based on the literature, many efforts by previous researchers on SFLA denote the next generations of basic SFLA with diverse structures for modified SFLA or hybrid SFLA. As well, an attempt is made to highlight these structures, their enhancements and advantages. Moreover, this paper considers top improvements on SFLA for solving multi-objective optimization problems, enhancing local and global exploration, avoiding being trapped into local optima, declining computational time and improving the quality of the initial population. The measured enhancements in SFLA are based on the statistical results obtained from 89 published papers and by considering the most common and effective modifications done by a large number of researchers. Finally, the quantitative validations address the SFLA as a robust algorithm employed in various applications which outperforms the other optimization algorithms.

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“…Das proposed a fuzzy multi-objective approach to solve the network reconfiguration problem [9]. Niknam et al [10][11][12] proposed three approach based on hybrid evolutionary algorithm (EA) for single-objective DFR problem. Olamaei et al proposed a cost-based compensation methodology for distribution feeder reconfiguration considering distributed generators [13,14].…”

Section: Introductionmentioning

confidence: 99%

An efficient multi-objective modified shuffled frog leaping algorithm for distribution feeder reconfiguration problem

Niknam

Azad‐Farsani

Nayeripour

2010

Euro. Trans. Electr. Power

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SUMMARYDistributed feeder reconfiguration (DFR) is an operation problem, which entails altering the topological structure of the distribution feeders by rearranging the status of switches in order to obtain an optimal configuration. This paper proposes a new evolutionary algorithm (EA) for solving the multi-objective distribution feeder reconfiguration (MDFR) problem. The proposed algorithm is an efficient multi-objective modified shuffled frog leaping algorithm (MMSFLA) that has been used to solve MDFR problem. A new frog leaping rule is proposed to improve the local exploration of the SFLA, which in turn improves the overall performance of the MSLFA. The main focus of this study is to minimize the real power loss, deviation of the nodes' voltages, and the number of switching operations. In the proposed MMSFLA, an external repository is considered to save non-dominated solutions found during the search process. Since the objective functions are not the same, a fuzzy clustering technique is used to control the size of the repository within the limits. Three distribution test feeders are considered to evaluate the feasibility and effectiveness of the proposed approach.

show abstract

A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration

Cited by 99 publications

References 16 publications

Frog Inspired Algorithms

Frog Inspired Algorithms

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

An efficient multi-objective modified shuffled frog leaping algorithm for distribution feeder reconfiguration problem

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