Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Abstract

Abstract-Krill herd algorith m (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, mo ment induced by other krill, fo raging motion and random physical d iffusion. These three key features of the algorith m provide a good balance between global and local search capability, which makes the algorith m very powerfu l. The objective is to minimize the distance of each krill fro m the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH A lgorith m. KHA with mutation and crossover is called as (KHAM C) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two imp roved variants KHAMC and KHAM CD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms. Index Terms-Krill Herd A lgorith m (KHA), mutation and crossover, neighborhood distance concept, unimodal function, multimodal function, economic load dispatch.

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