An efficient and robust artificial bee colony algorithm for numerical optimization

Xiang, Wei; An, Mei-qing

doi:10.1016/j.cor.2012.12.006

Cited by 146 publications

(62 citation statements)

References 21 publications

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“…I-ABC greedy makes use of greedy bee concept to find the best solution and enhanced convergence. X i a n g and A n [126] added four modifications to the basic ABC in order to enhance convergence performance, population diversity, exploration capability and avoiding local minima traps. The modifications such as chaotic initialization, roulette wheel based reverse selection, modified search equation and chaotic search make the ERABC as efficient and robust optimization algorithm.…”

Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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Section: Artificial Bee Colony Algorithmmentioning

confidence: 99%

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

Kumar

Jarial

2017

Cybernetics and Information Technologies

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“…Banharnsakun et al [24] presented an improved search equation that causes the solution to directly converge towards the best-so-far solution rather than through a randomly selected path. Xiang and An [25] developed an efficient and robust ABC algorithm (ERABC), in which a combined solution search equation is used to accelerate the search process. Xianneng and Yang [26] introduced a new ABC method with memory (ABCM) to guide the further foraging of the artificial bees by combining other search equations to select the best solution.…”

Section: Artificial Bee Colony Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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“…In this experiment, the precision thresholds are set as follows. for 1 6 , f f , 1.0 ε = ; for 2 f 10 ε = ; for 3 f , 5 10 ε − = ; for 4 9 10 , , f f f , [12], the initial value of rotation angle is 0.05π , and the mutation probability is 10 -3 . For QDPSO, based on Ref.…”

Section: Parameters Settingmentioning

confidence: 99%

Quantum-Inspired Bee Colony Algorithm

Li¹,

Sun²,

Li³

2015

OJOp

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To enhance the performance of the artificial bee colony optimization by integrating the quantum computing model into bee colony optimization, we present a quantum-inspired bee colony optimization algorithm. In our method, the bees are encoded with the qubits described on the Bloch sphere. The classical bee colony algorithm is used to compute the rotation axes and rotation angles. The Pauli matrices are used to construct the rotation matrices. The evolutionary search is achieved by rotating the qubit about the rotation axis to the target qubit on the Bloch sphere. By measuring with the Pauli matrices, the Bloch coordinates of qubit can be obtained, and the optimization solutions can be presented through the solution space transformation. The proposed method can simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experimental results show that the proposed method is obviously superior to the classical one for some benchmark functions.

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An efficient and robust artificial bee colony algorithm for numerical optimization

Cited by 146 publications

References 21 publications

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Quantum-Inspired Bee Colony Algorithm

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