An Improved Memetic Search in Artificial Bee Colony Algorithm

Artificial Bee Colony (ABC) is a swarm optimization technique. This algorithm generally used to solve nonlinear and complex problems. ABC is one of the simplest and up to date population based probabilistic strategy for global optimization. Analogous to other population based algorithms, ABC also has some drawbacks computationally pricey due to its sluggish temperament of search procedure. The solution search equation of ABC is notably motivated by a haphazard quantity which facilitates in exploration at the cost of exploitation of the search space. Due to the large step size in the solution search equation of ABC there are chances of skipping the factual solution are higher. For that reason, this paper introduces a new search strategy in order to balance the diversity and convergence capability of the ABC. Both employed bee phase and onlooker bee phase are improved with help of a local search strategy stimulated by memetic algorithm. This paper also proposes a new strategy for fitness calculation and probability calculation. The proposed algorithm is named as Improved Memetic Search in ABC (IMeABC). It is tested over 13 impartial benchmark functions of different complexities and two real word problems are also considered to prove proposed algorithms superiority over original ABC algorithm and its recent variants. Keywords— Artificial bee colony algorithm, Swarm intelligence, Evolutionary computation, Memetic algorithm