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Comparative Study of Different Modified Artificial Bee Colony Algorithm with Proposed ABC Algorithm
| Content Provider | CiteSeerX |
|---|---|
| Author | Maheshwari, Vani Dutta, Unmukh |
| Abstract | Abstract Swarm intelligence systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. Artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE) etc, are some example of swarm intelligence. In this work, an efficient modified version of ABC algorithm is proposed, where two additional operator crossover and mutation operator is used in the standard artificial bee colony algorithm. Here Crossover operator is used after the employed bee phase and mutation operator is used after scout bee phase of ABC algorithm and simulated results are compared with different modified version of artificial bee colony algorithms, like ABC with uniform mutation, ABC with crossover and mutation and Basic ABC algorithm. The simulated result showed that the proposed algorithm is better than all the modified version of ABC algorithm. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Abc Algorithm Simulated Result Mutation Operator Efficient Modified Version Particle Swarm Optimization Modified Version Basic Abc Algorithm Scout Bee Phase Uniform Mutation Swarm Intelligence Additional Operator Crossover Crossover Operator Artificial Bee Colony Algorithm Standard Artificial Bee Colony Algorithm Artificial Bee Colony Simple Agent Abstract Swarm Intelligence System Differential Evolution Employed Bee Phase Ant Colony Optimization |
| Content Type | Text |
