Discrete Krill herd algorithm-A bio-inspired meta-heuristics for graph based network route optimization

Abstract

Krill Herd Algorithm (KHA) is creature inspired meta-heuristic search algorithm, inspired by the tiny sea creature krill and its style of living, which can be utilized in optimization solution foundation of NP-Hard problems. In this paper we have adopted the various activities of the creature and described a discrete version of the Krill Herd Algorithm for the first time which is favorable for graph network based search and optimization problems. KHA is operated on a multi-parametric road graph for search of optimized path with respect to some parameters and evaluation function and the convergence rate is compared with Ant Colony Optimization (ACO) and Intelligent Water Drop (IWD) algorithms. The proposed KHA works well when it comes to decision making and path planning for graph based networks and other discrete event based optimization problems and works on the principle of various random exploration schemes following some parameters which decides whether to include a node/edge or not. Due to the dynamicity of the road network with several dynamic parameters, the optimized path tends to change with intervals, the optimized path changes and will bring about a near fair distribution of vehicles in the road network and withdraw the excessive pressure on the busy roads and pave the way for proper exploitation of the underutilized. © 2014 Springer International Publishing Switzerland.