Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems
Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant i...
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| Vydané v: | Engineering applications of artificial intelligence Ročník 36; s. 148 - 163 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.11.2014
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions.
•We propose a hybrid ABC algorithm so called VABC for numerical function optimization.•Inspiring from the PSO, the VABC improves the ABC׳s exploitation strategy.•The VABC considers a velocity value for each particle in the onlooker search equation.•The VABC is compared with ABC, PSO and the seven state-of-the-art hybrid methods.•The results show that the VABC has higher convergence speed and better search ability. |
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| AbstractList | Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions. Artificial bee colony (ABC) is a swarm optimization algorithm which has been shown to be more effective than the other population based algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). Since it was invented, it has received significant interest from researchers studying in different fields because of having fewer control parameters, high global search ability and ease of implementation. Although ABC is good at exploration, the main drawback is its poor exploitation which results in an issue on convergence speed in some cases. Inspired by particle swarm optimization, we propose a modified ABC algorithm called VABC, to overcome this insufficiency by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions. The experimental results tested on numerical benchmark functions show that the VABC has good performance compared with PSO and ABC. Moreover, the performance of the proposed algorithm is also compared with those of state-of-the-art hybrid methods and the results demonstrate that the proposed method has a higher convergence speed and better search ability for almost all functions. •We propose a hybrid ABC algorithm so called VABC for numerical function optimization.•Inspiring from the PSO, the VABC improves the ABC׳s exploitation strategy.•The VABC considers a velocity value for each particle in the onlooker search equation.•The VABC is compared with ABC, PSO and the seven state-of-the-art hybrid methods.•The results show that the VABC has higher convergence speed and better search ability. |
| Author | Moradi, Parham Imanian, Nafiseh Shiri, Mohammad Ebrahim |
| Author_xml | – sequence: 1 givenname: Nafiseh surname: Imanian fullname: Imanian, Nafiseh email: imanian@aut.ac.ir organization: Mathematics and Computer Science Department, Amirkabir University of Technology, Tehran, Iran – sequence: 2 givenname: Mohammad Ebrahim surname: Shiri fullname: Shiri, Mohammad Ebrahim email: shiri@aut.ac.ir organization: Mathematics and Computer Science Department, Amirkabir University of Technology, Tehran, Iran – sequence: 3 givenname: Parham surname: Moradi fullname: Moradi, Parham email: p.moradi@uok.ac.ir organization: Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran |
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| SubjectTerms | Algorithms Ant colony optimization Artificial bee colony Continuous optimization Convergence Hybrid optimization algorithm Mathematical analysis Mathematical models Optimization Particle swarm optimization Searching Swarm intelligence |
| Title | Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems |
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