An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering

• We improved the performance of krill herd algorithm.
• We applied the proposed algorithm IKH for data clustering.
• We compared the proposed method with other well known heuristic algorithms for function optimization and clustering using benchmark dataset.

Krill herd algorithm is a stochastic nature-inspired algorithm for solving optimization problems. The performance of krill herd algorithm is degraded by poor exploitation capability. In this study, we propose an improved krill herd algorithm (IKH) by making the krill the global search capability. The enhancement comprises of adding global search operator for exploration around the defined search region and thus the krill individuals move towards the global best solution. The elitism strategy is also applied to maintain the best krill during the krill update steps. The proposed method is tested on a set of twenty six well-known benchmark functions and is compared with thirteen popular optimization algorithms, including original KH algorithm. The experimental results show that the proposed method produced very accurate results than KH and other compared algorithms and is more robust. In addition, the proposed method has high convergence rate. The high performance of the proposed algorithm is then employed for data clustering problems and is tested using six real datasets available from UCI machine learning laboratory. The experimental results thus show that the proposed algorithm is well suited for solving even data clustering problems.