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Interactive Artificial Bee Colony (IABC) Optimization

Interactive Artificial Bee Colony (IABC) Optimization. Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, and Shu-Chuan Chu pwtsai@bit.kuas.edu.tw. Outline. Introduction Artificial Bee Colony (ABC) Algorithm Interactive Artificial Bee Colony (IABC) Experiments and Experimental Results

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Interactive Artificial Bee Colony (IABC) Optimization

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  1. Interactive Artificial Bee Colony (IABC) Optimization Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, and Shu-Chuan Chu pwtsai@bit.kuas.edu.tw

  2. Outline • Introduction • Artificial Bee Colony (ABC) Algorithm • Interactive Artificial Bee Colony (IABC) • Experiments and Experimental Results • Conclusions

  3. Introduction • Swarm Intelligence employs the collective behaviors in the animal societies to design algorithms. • In 2005, Karaboga proposed an Artificial Bee Colony (ABC), which is based on a particular intelligent behavior of honeybee swarms.

  4. Artificial Bee Colony (ABC) • ABC is developed based on inspecting the behaviors of real bees on finding nectar and sharing the information of food sources to the bees in the hive. • Agents in ABC: • The Employed Bee • The Onlooker Bee • The Scout

  5. Artificial Bee Colony (ABC) (2) • The Employed Bee:It stays on a food source and provides the neighborhood of the source in its memory. • The Onlooker Bee:It gets the information of food sources from the employed bees in the hive and select one of the food source to gathers the nectar. • The Scout:It is responsible for finding new food, the new nectar, sources.

  6. Artificial Bee Colony (ABC) (3) • Procedures of ABC: • Initialize (Move the scouts). • Move the onlookers. • Move the scouts only if the counters of the employed bees hit the limit. • Update the memory • Check the terminational condition

  7. Movement of the Onlookers • Probability of Selecting a nectar source: (1) Pi : The probability of selecting the ith employed bee S : The number of employed bees θi : The position of the ith employed bee : The fitness value

  8. Movement of the Onlookers (2) • Calculation of the new position: (2) • : The position of the onlooker bee. • t : The iteration number • : The randomly chosen employed bee. • j : The dimension of the solution • : A series of random variable in the range .

  9. Movement of the Scouts • The movement of the scout bees follows equation (3). (3) • r : A random number and

  10. Artificial Bee Colony (ABC) (4) • The Employed Bee • The Onlooker Bee • The Scout Record the best solution found so far

  11. Discussion • The movement of the onlookers is limited to the selected nectar source and the randomly selected source. • Suppose we find a way to consider more relations between the employed bees and the onlookers, we may extend the exploitation capacity of the ABC algorithm.

  12. Universal Gravitation • Universal Gravitation is an invisible force between objects. (4) • : The gravitational force heads from object 1 to 2. • G : The universal gravitational constant. • m : The mass of the object. • : The separation between the objects. • : The unit vector in the form of equation.

  13. Interactive Artificial Bee Colony • In Interactive Artificial Bee Colony (IABC), the mass in equation (4) is replaced by . • Euclidean distance is applied for calculating . • The normalization procedure is applied to the fitness values we used in equation (4) and the normalized fitness values are given as .

  14. Interactive Artificial Bee Colony (2) • After employing the universal gravitation into equation (2), it can be reformed as follows: (5) • By applying equation (5) and simultaneously considering the gravitation between the picked employed bee and n selected employed bees, it can be reformed again into equation (6). (6)

  15. Interactive Artificial Bee Colony (3)

  16. Experiments • To analyze the performances, the experiments are made with three well-known benchmark functions, and the results are compared with ABC and Particle Swarm Optimization (PSO).

  17. Experiments (2)

  18. Experiments (3) • Conditions: • Dimension of the solution: 50 • Runs for average: 30 • Iteration number: 5000 • Population size: 100

  19. Experiments (4) • To apply IABC for solving problems related to optimization, the number of the considered employed bee n should be predetermined. • In these experiments, the number of n is set to 4.

  20. Experimental Results

  21. Experimental Results (3)

  22. Experimental Results (2)

  23. Conclusions • IABC is proposed in this paper. • It leads in the concept of universal gravitation to the movement of onlooker bees in ABC, and it successfully increases the exploitation ability of ABC. • The performance of IABC, ABC and PSO are compared in the experiments, and the value of n with the best reaction is also discussed and analyzed.

  24. Thank You for Your Attention. • Any Question?

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