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ABC is an optimization technique, used in finding the best solution from all feasible solutions. However, there is still an insufficiency in ABC regarding improvement in exploitation and convergence speed. In order to improve the performance of ABC we used mean mutation operator (MMO), which uses a linear combination of Gaussian and Cauchy distributions. This convoluted distribution produces larger...
The Artificial Bee Colony (ABC) algorithm, proposed by Karaboga in 2005 for real-parameter optimization, is a recently introduced optimization algorithm which simulates the foraging behaviour of a bee colony. The proposed variant employs colony size (population size) reduction mechanism during the evolutionary process. Then modification is done to enhance the perturbation scheme. Further, in order...
Differential Evolution (DE) is a simple, efficient algorithm which has reportedly outperformed many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, one is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. The proposed scheme dynamically...
ABC (Artificial Bee Colony) is one of the most recent nature inspired algorithm (NIA) based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. In this paper, we propose a modified version of the ABC to improve its performance, in terms of converging to individual optimal point and...
Artificial Bee Colony or ABC is one of the newest additions to the class of population based Nature Inspired Algorithms (NIA). In the present study we suggest some modifications in the structure of basic ABC to further improve its performance. The corresponding algorithm proposed in the present study is named Intermediate ABC (I-ABC). In I-ABC, the potential food sources are generated by using the...
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