Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

Yazdani, Maziar; Jolai, Fariborz

doi:10.1016/j.jcde.2015.06.003

Cited by 405 publications

(184 citation statements)

References 50 publications

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“…Rajakumar [15] proposed an algorithm named "The Lion's Algorithm", which was based on the mating, territorial defense, and territorial takeover. In 2015, Yazdani and Jolai [6] proposed the Lion Optimization Algorithm (LOA), which was different from the previous algorithms. It was inspired by simulating the isolated life style and cooperative behaviors of lions, such as hunting, territory marking, migration, and the different life styles of the nomad and resident lions, in addition to mating and fighting.…”

Section: Related Workmentioning

confidence: 99%

“…Throughout hunting, if the new position of the hunter is better than its previous position, which means that it has improved its own fitness, the prey will escape from the hunter, and the new position of the prey will be [6]:…”

Section: Related Workmentioning

confidence: 99%

“…In this study, a new task scheduling algorithm is proposed for cloud environment using the concept of lion optimization algorithm (LOA), which was proposed in [6]. LOA is a natureinspired algorithm based on the special lifestyle of lions and their cooperative behaviors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Almezeini¹,

Hafez²

2017

ijacsa

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Almezeini¹,

Hafez²

2017

ijacsa

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“…The techniques used for this purpose are a clustering approach, HKFC and a feed forward neural network. MKFC extends Fuzzy C-Means (FCM) [28] by adopting multiple kernel functions in its membership degree for clustering. By utilizing the averaging process of centroids, MKFC can compute the missing attributes, which are then, combined with the values generated by the neural network, for the effective data imputation.…”

Section: Figure 1: Block Diagram Of Proposed Hkfc and Flnn Based Missmentioning

confidence: 99%

Hybrid Kernel Fuzzy Clustering with Feed Lion Neural Network for Missing Data Imputation and Classification

Al¹

2017

IJCSEITR

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A common issue in many practical applications associated with pattern classification is data incomplete or missing data due to various reasons that differ based on the applications. Missing data imputation is a promising approach used to handle this issue, which fills the missing attributes with estimated values following several techniques. This paper proposes a new strategy for data imputation and classification using a hybrid prediction model that combines Hybrid Kernel Fuzzy Clustering (HKFC) and Feed Lion Neural Network (FLNN). FLNN considers the missingattributes as the class attributes to predict the missing data, which will also be computed using HKFC and finally, the combined effect of these two approaches provides the missing data. FLNN is designed for the classification by modifying

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“…This paper organ ized as below: section I deals with in t rodu ct ion to o pt imizat io n tech n iq ues , s ect io n II p resents krill h erd alg o rit h m and its variants . [17] 1983 Simulated Annealing [5] 2011 Cuckoo Search Algorithm [18] 1989 T abu Search [6] 2012 Krill Herd Algorithm [19] 1995 Particle Swarm Optimization [7] 2013 Social Spider optimization [20] 1996 Ant Colony Optimization [8] 2013 Backtracking Search Algorithm [21] 2001 Harmony Search Algorithm [9] 2014 Grey Wolf Optimization [22] 2002 Estimation of Distribution Algorithm [10] 2014 Symbiotic organism Search Algorithm [23] 2002 Bacterial Foraging Algorithm [11] 2015 Lion Optimization Algorithm [24] 2005 Honey Bee Mating Optimization Algorithm [12] 2015 Stochastic fractal Search [25] 2007 Intelligent Water Drops [13] …”

Section: Introductionmentioning

confidence: 99%

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Agrawal¹,

Pandit²,

Dubey³

2016

IJISA

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Abstract-Krill herd algorith m (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, mo ment induced by other krill, fo raging motion and random physical d iffusion. These three key features of the algorith m provide a good balance between global and local search capability, which makes the algorith m very powerfu l. The objective is to minimize the distance of each krill fro m the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH A lgorith m. KHA with mutation and crossover is called as (KHAM C) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two imp roved variants KHAMC and KHAM CD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms. Index Terms-Krill Herd A lgorith m (KHA), mutation and crossover, neighborhood distance concept, unimodal function, multimodal function, economic load dispatch.

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Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

Cited by 405 publications

References 50 publications

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Hybrid Kernel Fuzzy Clustering with Feed Lion Neural Network for Missing Data Imputation and Classification

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

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