Logarithmic Spiral Based Local Search in Artificial Bee Colony Algorithm

Abstract

Artificial bee colony (ABC) algorithm is recent swarm intelligence based meta-heuristic that is developed to solve complex real problems which are difficult to solve by the available deterministic strategies. It mimics the natural behaviour of real honey bees while searching for food sources. The performance of ABC depends on the size of step during position update process, that is a combination of the arbitrary component \(\phi _{ij}\) and a difference vector between the current solution and an arbitrarily identified solution. The high value of \(\phi _{ij}\) and high difference between the vectors in the step generation process may generate the large size step which may leads to the skipping of true solution. Therefore, to avoid this situation a logarithmic spiral based local search strategy, namely logarithmic spiral local search (LSLS) is planned and incorporated with the ABC. The proposed hybridized ABC is named as logarithmic spiral based ABC (LSABC). To demonstrate the efficiency and accurateness of the LSABC, it is tested over 10 popular benchmarks functions and outcomes are equated with ABC, Modified ABC, and Best-so-far ABC. The reported results showed that the proposed LSABC is a new viable variation of ABC algorithm.