ARTIFICIAL BEE COLONY OPTIMIZATION
Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karabogain 2005, motivated by the intelligent behaviour of honey bees. It is as simple as Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, and uses only common control parameters such as colony size and maximum cycle number. ABC as an optimization tool provides a population-based search procedure in which individuals called foods positions are modified by the artificial bees with time and the bee’s aim is to discover the places of food sources with high nectar amount and finally the one with the highest nectar. In ABC system, artificial bees fly around in a multidimensional search space and some (employed and onlooker bees) choose food sources depending on the experience of themselves and their nest mates, and adjust their positions. Some (scouts) fly and choose the food sources randomly without using experience. If the nectar amount of a new source is higher than that of the previous one in their memory, they memorize the new position and forget the previous one. Thus, ABC system combines local search methods, carried out by employed and onlooker bees, with global search methods, managed by onlookers and scouts, attempting to balance exploration and exploitation process.
Foraging principles of natural honey bees
Two techniques have been developed for the process of carrying out the collection of honey bees nectar
Recruitment deals with the participation of bees in the execution and the leave of food sources after its usage is Abandonment. The honey bees are identical in shape and size but we can categorize these bees in terms of their working pattern or responsibility during the process they carry. The bees those are now exploiting a food source known as employer bees. The employer bees take the information regarding the sources and after frequent to the hive they carve up the information with previous bees waiting in the hive. The bees those are coming up for the information of employer are known as onlooker bees. To crack the information an employer uses a unique technique known as waggle dance. The waggle dance is a physical society of the bees and is necessary for the foragers as it show a few significant information concerning food sources similar to – direction, distance and value of nectar. Following a convinced collecting period some food sources become ineffective and the abandonment process takes place. The bees those go the hive to discover new food sources called as scout bees. Figure would assist now to appreciate the vital information of foraging process. In figure, let us judge an employer bee presently exploiting a food source. Following his returning to the hive by E he may contain three options to choose. Path E1 that guide him to stay continue to collecting, path E2 if he determined to divide the information by dancing and E3 if abandonment of the food source has completed. On the other hand an onlooker waits in the hive and observes (position – O) the waggle dance to get recruited. The path S denotes the arbitrary look for of undiscovered food source and explored by scout bees.
The essential ABC algorithm can be divided into three stages:
- Employed bee phase
- Onlooker bee phase
- Scout bee phase
Initially the food sources need to be particular. A set of feasible explanation of a difficulty can be occupied as the food sources. Thus, if we have a D dimensional problem (where i=1, 2… D) Afterwards the decision variables act as the members of a single food sources. The assessment of these choice variables are randomly generated within the variable bounds if they have any. For a typical monthly water supply difficulty of a reservoir, a set of release options (consisting 12 values for each month) can be occupied as food sources. That means a single source contain 12 consecutive monthly release options. The employed bee experienced the fitness values of pre distinct no. of food sources and records the information about them. The quality of each source is the fitness value of the objective function considered to reduce or maximize according to service criterion of the reservoir. Employed bees decide a food source and arbitrarily decide a candidate solution within the source to inform it provided that new values by using equation.
Vij = xij+Øij (xij-xik)
Here, xij represents the existing candidate solution of a source and xik symbolize other arbitrarily selected answer except must be taken from different neighbour source. Øij is a random number between [-1 to 1].
Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm  is an optimization algorithm based on the intelligent behaviour of honey bee swarm.
In the ABC algorithm, the colony of artificial bees contains three groups of bees: employed bees, onlookers and scouts. A bee waiting on the dance area for making decision to choose a food source, is called an onlooker and a bee going to the food source visited by itself previously is named an employed bee. A bee carrying out random search is called a scout. In the ABC algorithm, first half of the colony consists of employed artificial bees and the second half constitutes the onlookers. For every food source, there is only one employed bee. In other words, the number of employed bees is equal to the number of food sources around the hive. The employed bee whose food source is exhausted by the employed and onlooker bees becomes a scout. The main steps of the algorithm are given below :Initialize.
(a) Place the employed bees on the food sources in the memory;
(b) Place the onlooker bees on the food sources in the memory;
(c) Send the scouts to the search area for discovering new food sources.
- UNTIL (requirements are met).