MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach

Abbass, Hussein A.

doi:10.1109/cec.2001.934391

Cited by 233 publications

(168 citation statements)

References 5 publications

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“…The queen mates multiple times but the drone, inevitably, only once. These features make bee mating the most spectacular mating among insects [5].…”

Section: Step 1: Mating Flight Of Queen Bees With Dronesmentioning

confidence: 99%

Evaluating Performance of Honey Bee Mating Optimization

Karimi

Mostoufi

Sotudeh‐Gharebagh

2013

J Optim Theory Appl

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The honey bee mating optimization (HBMO) algorithm is presented and tested with various test functions, and its performance is compared with the genetic algorithm (GA). It is shown that the HBMO algorithm can overcome the weaknesses of the GA. The HBMO converges faster than the GA. Even when the HMBO starts from a more improper initial condition than the GA, it can reach a better solution in a smaller number of function evaluations. Furthermore, in some cases, the GA was not able to reach the global minimum.

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“…The queen mates multiple times but the drone, inevitably, only once. These features make bee mating the most spectacular mating among insects [5].…”

Section: Step 1: Mating Flight Of Queen Bees With Dronesmentioning

confidence: 99%

Evaluating Performance of Honey Bee Mating Optimization

Karimi

Mostoufi

Sotudeh‐Gharebagh

2013

J Optim Theory Appl

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“…for the reason that all drones are naturally haploid, a genotype marker may be employed to randomly mark half of the genes, exit the other half unmarked. In this case, only the unmarked genes are those that form a sperm to be by chance used in the mating process [12].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Contrary to the reality that there are various algorithms that are based on the foraging performance of the bees, the main algorithm proposed based on the marriage behavior is the Honey Bees Mating Optimization algorithm (HBMO), so as to was existing [7,8].This paper studies the GEP problem which is a multipart problem. Honey bee mating optimization (HBMO) algorithm is used as an optimization means for solving this difficult and nonlinear problem.…”

Section: Introductionmentioning

confidence: 99%

Optimization Generation Expansion Planning by HBMO

hejrati¹,

hejrati²,

taheri.moghadam³

2012

IJCA

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The generation expansion planning (GEP) problem is a largescale mixed integer nonlinear programming (MINLP) problem cited as one of the most complex optimization problems. In this paper, an application of honey bee mating optimization for solving the generation expansion planning problem is presented. In the formulation, the objective is to minimize investment cost. The GEP problem considered is a test system for a six-year planning horizon having five types of candidate units. The results are compared and validated.

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“…Different techniques have been used to build HH systems of this class. Algorithms used to achieve this include, for example: tabu search (Burke et al, 2003b), case-based reasoning (Burke et al, 2006b), genetic algorithms (Cowling et al, 2002), ant-colony systems (Silva et al, 2005), and even algorithms inspired to marriage in honey-bees (Abbass, 2001).…”

Section: Hyper-heuristics Gp and Satmentioning

confidence: 99%

Evolving Effective Incremental Solvers for SAT with a Hyper-Heuristic Framework Based on Genetic Programming

Bader-El-Den

Poli

2008

Genetic and Evolutionary Computation

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Hyper-heuristics could simply be defined as heuristics to choose other heuristics. In other words, they are methods for combining existing heuristics to generate new ones. In this paper, we use a grammar-based genetic programming hyperheuristic framework. The framework is used for evolving effective incremental solvers for SAT. The evolved heuristics perform very well against well-known local search heuristics on a variety of benchmark SAT problems.

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MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach

Cited by 233 publications

References 5 publications

Evaluating Performance of Honey Bee Mating Optimization

Evaluating Performance of Honey Bee Mating Optimization

Optimization Generation Expansion Planning by HBMO

Evolving Effective Incremental Solvers for SAT with a Hyper-Heuristic Framework Based on Genetic Programming

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