A novel binary artificial bee colony algorithm based on genetic operators
This study proposes a novel binary version of the artificial bee colony algorithm based on genetic operators (GB-ABC) such as crossover and swap to solve binary optimization problems. Integrated to the neighbourhood searching mechanism of the basic ABC algorithm, the modification comprises four stag...
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| Published in: | Information sciences Vol. 297; pp. 154 - 170 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
10.03.2015
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| ISSN: | 0020-0255 |
| Online Access: | Get full text |
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| Abstract | This study proposes a novel binary version of the artificial bee colony algorithm based on genetic operators (GB-ABC) such as crossover and swap to solve binary optimization problems. Integrated to the neighbourhood searching mechanism of the basic ABC algorithm, the modification comprises four stages: (1) In neighbourhood of a (current) food source, randomly select two food sources from population and generate a solution including zeros (Zero) outside the population; (2) apply two-point crossover operator between the current, two neighbourhood, global best and Zero food sources to create children food sources; (3) apply swap operator to the children food sources to generate grandchildren food sources; and (4) select the best food source as a neighbourhood food source of the current solution among the children and grandchildren food sources. In this way, the global–local search ability of the basic ABC algorithm is improved in binary domain. The effectiveness of the proposed algorithm GB-ABC is tested on two well-known binary optimization problems: dynamic image clustering and 0–1 knapsack problems. The obtained results clearly indicate that GB-ABC is the most suitable algorithm in binary optimization when compared with the other well-known existing binary optimization algorithms. In addition, the achievement of the proposed algorithm is supported by applying it to the CEC2005 benchmark numerical problems. |
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| AbstractList | This study proposes a novel binary version of the artificial bee colony algorithm based on genetic operators (GB-ABC) such as crossover and swap to solve binary optimization problems. Integrated to the neighbourhood searching mechanism of the basic ABC algorithm, the modification comprises four stages: (1) In neighbourhood of a (current) food source, randomly select two food sources from population and generate a solution including zeros (Zero) outside the population; (2) apply two-point crossover operator between the current, two neighbourhood, global best and Zero food sources to create children food sources; (3) apply swap operator to the children food sources to generate grandchildren food sources; and (4) select the best food source as a neighbourhood food source of the current solution among the children and grandchildren food sources. In this way, the global-local search ability of the basic ABC algorithm is improved in binary domain. The effectiveness of the proposed algorithm GB-ABC is tested on two well-known binary optimization problems: dynamic image clustering and 0-1 knapsack problems. The obtained results clearly indicate that GB-ABC is the most suitable algorithm in binary optimization when compared with the other well-known existing binary optimization algorithms. In addition, the achievement of the proposed algorithm is supported by applying it to the CEC2005 benchmark numerical problems. |
| Author | Karaboga, Dervis Ozturk, Celal Hancer, Emrah |
| Author_xml | – sequence: 1 givenname: Celal surname: Ozturk fullname: Ozturk, Celal email: celal@erciyes.edu.tr – sequence: 2 givenname: Emrah surname: Hancer fullname: Hancer, Emrah email: emrahhancer@erciyes.edu.tr – sequence: 3 givenname: Dervis surname: Karaboga fullname: Karaboga, Dervis email: karaboga@erciyes.edu.tr |
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| SubjectTerms | Algorithms Artificial bee colony Binary optimization Children Dynamic clustering Foods Genetic algorithm Genetics Knapsack problem Mathematical models Operators Optimization Swarm intelligence |
| Title | A novel binary artificial bee colony algorithm based on genetic operators |
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