Volleyball Premier League Algorithm

Moghdani, Reza; Salimifard, Khodakaram

doi:10.1016/j.asoc.2017.11.043

Cited by 242 publications

(118 citation statements)

References 91 publications

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“…In this experiment, the VPL (volleyball premier league) algorithm [60] is compared with the SGO algorithm. For performance comparison of algorithms, 23 classical benchmarkfunctions are considered.…”

Section: Methodsmentioning

confidence: 99%

“…We have directly derived results of the VPL algorithm from [60], and for results of the SGO algorithm, the codes are implemented in MATLAB 2016a. Experiments are conducted on an Intel Core i5, 8 GB memory laptop in Windows 10 environment.…”

Section: Methodsmentioning

confidence: 99%

“…The basic of SSOA is in [30]. The basic of HHO is in [31], and basic of VPL is in [60]. SELO, LAPO, and GROM can be followed in [61,79,80], respectively.…”

Section: Ssa Goa Lapo Grom Boa Ssoa Vpl Hho and Selo Algorithmsmentioning

confidence: 99%

“…Human based algorithms are mainly inspired by behaviors of human. Some of the popular algorithms are teaching-learning-based optimization (TLBO) [32], harmony search (HS) [33], Tabu (Taboo) search (TS) [34][35][36], group search optimizer (GSO) [37,38], imperialist competitive algorithm (ICA) [39], league championship algorithm (LCA) [40], firework algorithm [41], colliding bodies optimization (CBO) [42], interior search algorithm (ISA) [43], mine blast algorithm (MBA) [44], soccer league competition (SLC) algorithm [45], seeker optimization algorithm (SOA) [46], social-based algorithm (SBA) [47], exchange market algorithm (EMA) [48], and group counselling optimization (GCO) algorithm [49,50], social emotional optimization (SEO) [51], ideology algorithm (IA) [52], social learning optimization (SLO) [53], social group optimization (SGO) [54,55], election algorithm (EA) [56], cultural evolution algorithm (CEA) [57], cohort intelligence (CI) [58], anarchic society optimization (ASO) [59], volleyball premier league algorithm (VPL) [60], socio evolution and learning optimization algorithm (SELO) [61].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A comparative study of social group optimization with a few recent optimization algorithms

Naik

Satapathy

2020

Complex Intell. Syst.

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From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The basic of SSOA is in [30]. The basic of HHO is in [31], and basic of VPL is in [60]. SELO, LAPO, and GROM can be followed in [61,79,80], respectively.…”

Section: Ssa Goa Lapo Grom Boa Ssoa Vpl Hho and Selo Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative study of social group optimization with a few recent optimization algorithms

Naik

Satapathy

2020

Complex Intell. Syst.

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“…Teaching-Learning-Based optimization [44] is a popular one belonging to this category which is formulated by following the improving procedure of class grade. Apart form this, some other important methods of this category include Society and civilization [45], League championship algorithm [46], Fireworks algorithm [47], Tug of war optimization [48], Volleyball Premier League algorithm [49].…”

Section: Related Workmentioning

confidence: 99%

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

Guha¹,

Ghosh²,

Ghosh³

et al. 2020

Preprint

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A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using Wilcoxon rank-sum, Friedman and Kruskal Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms.

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A novel ameliorated Harris hawk optimizer for solving complex engineering optimization problems

Mahapatra

Dey

Raj

2021

Int J of Intelligent Sys

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This article presents a metaheuristic algorithm namely Ameliorated Harris Hawk Optimizer (AHHO) to solve multiconstrained optimization problems. AHHO mimics the hunting behavior of Harris Hawks and is modified to draw a synergy between exploration and exploitation by conducting neighborhood search of already visited iterations utilizing random exploratory forage and local random forage. The oppositional based learning has been incorporated to generate adequate diversity among solutions. The proposed algorithm is substantiated on a set of 23 standard benchmark functions to test its characteristics in finding optimal solutions. The algorithm is mathematically modeled in the current work to obtain solution for voltage constrained reactive power dispatch (VCRPD) problem for IEEE 57 and 118 bus systems. The proposed method of VCRPD is implemented first with shunt capacitors as var sources and then replacing them with static var compensator (SVC) for improved performance. The optimal placement of SVC is determined by fast voltage stability index method. The proposed AHHO algorithm reduces the transmission losses by 9.42% with shunt capacitors in IEEE 57 bus and 10.344% in IEEE 118 bus system. Similarly, transmission loss with SVC compensated system is reduced by 12.86% in IEEE 57

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Volleyball Premier League Algorithm

Cited by 242 publications

References 91 publications

A comparative study of social group optimization with a few recent optimization algorithms

A comparative study of social group optimization with a few recent optimization algorithms

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

A novel ameliorated Harris hawk optimizer for solving complex engineering optimization problems

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