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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

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
Outline
  • Introduction
  • Artificial Bee Colony (ABC) Algorithm
  • Interactive Artificial Bee Colony (IABC)
  • Experiments and Experimental Results
  • Conclusions
introduction
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.
artificial bee colony abc
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
artificial bee colony abc 2
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.
artificial bee colony abc 3
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
movement of the onlookers
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

movement of the onlookers 2
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 .
movement of the scouts
Movement of the Scouts
  • The movement of the scout bees follows equation (3).

(3)

    • r : A random number and
artificial bee colony abc 4
Artificial Bee Colony (ABC) (4)
  • The Employed Bee
  • The Onlooker Bee
  • The Scout

Record the best solution found so far

discussion
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.
universal gravitation
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.
interactive artificial bee colony
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 .
interactive artificial bee colony 2
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)

experiments
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).
experiments 3
Experiments (3)
  • Conditions:
    • Dimension of the solution: 50
    • Runs for average: 30
    • Iteration number: 5000
    • Population size: 100
experiments 4
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.
conclusions
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.