**Author details**

location, randomness and chemotactic step size. The big difference between PSO is that not only global best, but also global worst is also evaluated in the algorithm. Among *E. coli* cells, the least healthy one, eventually die. Each of the healthier bacteria splits into two bacteria, which are then placed in the same location. This keeps the swarm size constant. The hybrid algorithm using PSO and BFOA optimized PI controller, for multiobjective load frequency [41]. The authors developed a hybrid PSO with firefly, also developed a hybrid PSO with

ACO is inspired from the pheromone trails of ants. The first version improved by Dorigo is called ant system [43]. Although most of the ant species are blind, they can find the shortest path from their nest to food source using swarm intelligence. Ants are located in random positions in the solution space and moves with pheromone trail and randomness. In the first iterations, ants move stochastically. Eventually, pheromone increases in the path used most because ants prefer the path that contains more pheromone. This means that "Trace me." In order not to get trapped to the local convergence, pheromone evaporates related to time. ACO is generally used for the solution of combinatorial optimization problem solution such as travelling salesman problem

(TSP) and some network problems. Hybrid PSO with ACO [44] solves routing problem.

ABC optimization is developed by Karaboga in 2005. ABC is also a swarm intelligence algorithm based on the foraging behavior of honey bee swarms. The artificial bee colonies in the ABC algorithm consists of three groups: employed bees, onlookers and scouts. Employed bees search for food source and sharing this information to recruit onlooker bees. Onlooker bees select better food sources from those employed bees and further search around the selected food source. If a food source is not improved by some iteration, this employed bee will become a scout bee to search randomly for new food sources. In [45], a hybrid algorithm is developed combining PSO and ABC. Since PSO is fast convergent algorithm and ABC is slow convergent algorithm, hybrid algorithm uses the powerful sides of each algorithm.

CS is also another swarm intelligence algorithm inspired from cuckoos. CS is based on the interesting breeding behavior such as brood parasitism of certain species of cuckoos in combination with L'evy flight behavior of some birds. CS is successful for finding optimum values of multimodal functions. A hybrid algorithm is proposed finding for optimal design of multiband stop filters [46].

After GA, PSO is the first and foremost algorithm which is the most successful algorithm especially for the solution of the continuous optimization problems. Subsequent algorithms using swarm intelligence nearly have the same ideas. All of them are population based, and

BFOA, for the detection of BBB. The same classifier is used in the study [42].

**3.3. Ant colony optimization**

4 Particle Swarm Optimization with Applications

**3.4. Artificial bee colony**

**3.5. Cuckoo search**

**4. Conclusion**

Pakize Erdogmus

Address all correspondence to: pakizeerdogmus@duzce.edu.tr

Engineering Faculty, Computer Engineering Department, Duzce University, Duzce, Turkey
