Artificial bee colony optimization for the quadratic assignment problem

Dökeroğlu, Tansel; Sevinç, Ender; Çoşar, Ahmet

doi:10.1016/j.asoc.2019.01.001

Cited by 91 publications

(44 citation statements)

References 43 publications

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“…In [44], chaos and quantum theory were used to improve the performance of ABC. Dokeroglu et al [45] used a parallel ABC variant to optimize the quadratic assignment problem. Kishor et al [46] presented a multi-objective ABC based on non-dominated sorting.…”

Section: Related Workmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Xiao

Wang

et al. 2019

Mathematics

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Artificial bee colony is a powerful optimization method, which has strong search abilities to solve many optimization problems. However, some studies proved that ABC has poor exploitation abilities in complex optimization problems. To overcome this issue, an improved ABC variant based on elite strategy and dimension learning (called ABC-ESDL) is proposed in this paper. The elite strategy selects better solutions to accelerate the search of ABC. The dimension learning uses the differences between two random dimensions to generate a large jump. In the experiments, a classical benchmark set and the 2013 IEEE Congress on Evolutionary (CEC 2013) benchmark set are tested. Computational results show the proposed ABC-ESDL achieves more accurate solutions than ABC and five other improved ABC variants.

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Section: Related Workmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Xiao

Wang

et al. 2019

Mathematics

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“…Dokeroglu et al [27] proposed an island parallel ABC to solve the Quadratic Assignment Problem (QAP), in which Tabu search was used to balance exploitation and exploration. Ni et al [28] used an improved version of ABC for optimizing cumulative oil steam ratio.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Zhou

Wang

2019

Mathematical Problems in Engineering

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To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.

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“…Meanwhile, the quality of nectar of a food source represents the quality of the associated solution [29]. Lastly, in scouts bee phase, a random selection process carried out by scouts' bee [29,30,31], the abandonment counters of all employed bees are verified with a predefined limit [26,27,28,30]. Following is the basic step of the ABC algorithm:…”

Section: B Artificial Bee Colony (Abc)mentioning

confidence: 99%

Methods in Forecasting Water Used and Electricity Production at Hydropower Plants

Joe*,

Yuzainee,

Zaini

et al. 2019

IJRTE

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An energy system in a country has complex impacts on its economy. A retrenchment of energy supply influences economic activity such as distribution and saving of energy, as well as changes in technology to emphasize energy efficiency. Despite the advantages of hydropower developments in power generation over the past decades, highly controversial issues due to various social and environmental concerns are still debatable. A challenge for hydropower developers and operators, as well as government planners and regulators, is to develop tools that promote good practice and sustainable hydropower projects in energy security. In the future, both the scarcity of water and the cost of energy will likely to become limiting factors for economic and population growth, particularly in Malaysia, where the population is projected to grow dramatically. Various climate change models suggested that clean water supplies may decrease significantly. Therefore, integrated planning between the energy and water sectors will be essential to meet the rising demands of both resources. The purpose of this study is to review and identify the method that can deal with historical data and current practices at the hydropower plants to predict future electricity production due to the predicted water used in hydropower plants at the study areas. Various available methods to forecast water used and the electricity generated in hydropower plants have been identified and discussed in this paper.

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Artificial bee colony optimization for the quadratic assignment problem

Cited by 91 publications

References 43 publications

An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

An Improved Artificial Bee Colony Algorithm Based on Elite Strategy and Dimension Learning

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Methods in Forecasting Water Used and Electricity Production at Hydropower Plants

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