Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization

Bozorg‐Haddad, Omid; Afshar, Abbas; Mariňo, Miguel A.

doi:10.1007/s11269-005-9001-3

Cited by 329 publications

(147 citation statements)

References 11 publications

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“…The results compared very well with similar heuristic methods as well as global optimal results. Bozorg Haddad et al (2006) proposed Honey-Bees Mating Optimization (HBMO) algorithm, based on Abbass (2001aAbbass ( , 2001c, to solve highly non-linear constrained and unconstrained real valued mathematical models. The performance of the HBMO was tested on several constrained and unconstrained mathematical optimization functions and compared with the results obtained by genetic algorithm.…”

Section: Review and Categorization Of Studies On Artificial Bee Systemsmentioning

confidence: 99%

Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

lu¹,

Özbakır²,

Tapkan³

2007

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

155

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Section: Review and Categorization Of Studies On Artificial Bee Systemsmentioning

confidence: 99%

Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

lu¹,

Özbakır²,

Tapkan³

2007

Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

155

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“…The honey-bee mating optimization (HBMO) algorithm has been fully described in Bozorg Haddad et al (2006) and Afshar et al (2007). A brief discussion of the algorithm is presented in this paper.…”

Section: Hbmo Algorithmmentioning

confidence: 99%

“…Bozorg Haddad et al (2006) demonstrated the efficiency and applicability of the HBMO algorithm by applying it to well-known mathematical optimization problems and compared the final solutions with those from analytical methods and genetic algorithms (GAs).…”

Section: Introductionmentioning

confidence: 99%

Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs

Bozorg‐Haddad

Afshar

Mariňo

2008

Journal of Hydroinformatics

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The honey-bee mating process is considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is applied to extract the linear monthly operation rules of reservoirs for both irrigation and hydropower purposes. The release rules for each month are considered as a linear function of the reservoir past-month-end storage as well as current monthly inflow to the reservoir. In such a case, the decision variables are 36 for each problem and are set so that water supply deficits are minimized. In both irrigation and hydropower purposes, 60-480 months are considered and results are compared to those from the nonlinear programming solver of the LINGO 8.0 software. The approach and the rules tend to be very promising and denote the capability of the proposed HBMO algorithm in solving reservoir operation problems. Furthermore, the results indicated that, by using the near-optimal solution from the HBMO as a starting point for the NLP solver, the obtained objective function value was enhanced significantly and a better local optimum was found.

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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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Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization

Cited by 329 publications

References 11 publications

Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem

Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

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