A new tribe modified shuffled frog leaping algorithm for multi‐objective distribution feeder reconfiguration considering distributed generator units

Abstract

In this paper, an efficient Tribe-Modified Shuffled Frog Leaping Algorithm (T-MSFLA) is presented to solve Multi-objective Distribution Feeder Reconfiguration (MDFR) problem with regard to Distributed Generator (DG) units. The Distribution Feeder Reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DG units. The main focus of this study is to minimize the real power loss, deviation of the nodes' voltage, and emission produced by DG units and distribution companies (substation buses), and total cost of the active power generated by DG units and distribution companies, so that radiality of the network is maintained. In this paper, the max-min method has been used to preserve the radiality of network. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective problem. This paper presents a new T-MSFLA algorithm for the MDFR problem. In the proposed algorithm (T-MSFLA) at first we modify the frog leaping rule of shuffled frog leaping algorithm, the modified algorithm is MSFLA, and then a Tribe-MSFLA is used to prevent the prematurity of MSFLA. We consider an external repository 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. The proposed algorithm is tested on three distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization.

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

“…According to the results reported by Niknam et al (2011aNiknam et al ( , b, 2012, Malekpour et al (2012), Seyedi et al (2011), Gitizadeh et al (2012), and Sardou et al (2012) as the objective functions are indefinite, a clustering procedure using fuzzy set could be applied to organize the repository's size to distinguish the best compromise solution using the fuzzy membership function. The membership function of objective functions for each individual in the repository is considered as follows:…”

“…This MSFLA was applied to solve minimum spanning tree problems.Shayanfar et al (2010a, b) utilized a MSFLA to enhance local search. The MSFLA presented in that paper applied a new equation for updating the worst frog's position.Compared to multi-objective optimization problems,Malekpour et al (2012),Niknam et al (2011aNiknam et al ( , 2012 and developed a new MSFLA to handle multi-objective problems. The proposed MSFLA applied a fuzzy clustering technique to handle and normalize various objective functions.…”

“…Furthermore,Niknam et al (2011a) employed a new mutation for reducing computational time, raising solutions' quality, as well as avoiding being trapped in the local optima.Moreover,Niknam et al (2012) used a novel MSFLA by modifying frog leaping rule in SFLA and Tribe-MSFLA was employed to prevent the prematurity. Each memeplex is considered as a tribe in Tribe-MSFLA.…”

Shuffled differential evolution for economic dispatch with valve point loading effects

“…Recently, SFLA and its variants have been successfully applied to various fields of power system optimization [26][27][28][29]. Specifically, an efficient multi-objective modified shuffled frog leaping algorithm (MMSFLA) used to solve distribution feeder reconfiguration (DFR) problem in [26].…”

MKF-Firefly: Hybridization of Firefly and Multiple Kernel-Based Fuzzy C-Means Algorithm

“…Very recently, optimization algorithm has been included for clustering process. Here, the cluster centroids are found out through the heuristic search over the input data space using different search algorithms like, genetic algorithm, particle swarm optimization, cuckoo search, group search optimization, firefly algorithm and so on [4,14,15]. These heuristics algorithms are tried to find the centroids based on the different objective function which considers the kernel distance, the mean square distance and various clustering validity function.…”