Enhanced leadership-inspired grey wolf optimizer for global optimization problems
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ORIGINAL ARTICLE
Enhanced leadership‑inspired grey wolf optimizer for global optimization problems Shubham Gupta1 · Kusum Deep1 Received: 26 January 2019 / Accepted: 5 June 2019 © Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract Grey wolf optimizer (GWO) is a recently developed population-based algorithm in the area of nature-inspired optimization. The leading hunters in GWO are responsible for exploring the new promising regions of the search space. However, in some circumstances, the classical GWO suffers from the problem of premature convergence due to the stagnation at sub-optimal solutions. The insufficient guidance of search in GWO leads to slow convergence. Therefore, to alleviate from all the above issues, an improved leadership-based GWO called GLF–GWO is introduced in the present paper. In GLF–GWO, the leaders are updated through Levy-flight search mechanism. The proposed GLF–GWO algorithm enhances the search efficiency of leading hunters in GWO and provides better guidance to accelerate the search process of GWO. In the GLF–GWO algorithm, the greedy selection is introduced to avoid their divergence from discovered promising areas of the search space. To validate the efficiency of the GLF–GWO, the standard benchmark suite IEEE CEC 2014 and IEEE CEC 2006 are taken. The proposed GLF–GWO algorithm is also employed to solve some real-engineering problems. Experimental results reveal that the proposed GLF–GWO algorithms significantly improve the performance of the classical version of GWO. Keywords Numerical optimization · Swarm intelligence · No free lunch theorem · Levy-flight search
1 Introduction Swarm Intelligence has become an interesting and emerging field in the area of numerical optimization that has been used widely to solve many real-world problems. Swarm intelligence is based on the collaborative behavior of various species such as ants, whales, bees, wolves, and many others. Swarm intelligence-based algorithms start with a randomly generated population and iteratively utilize the social learning ability of creatures to find a global solution of the optimization problem. There are many optimization techniques available in the literature based on the intelligent and collective behavior of creatures and used when the conventional optimization techniques fail to solve an optimization problem. These techniques are also known as natureinspired optimization techniques, as they are designed for * Shubham Gupta [email protected] Kusum Deep [email protected] 1
Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand 247667, India
the simulation of nature’s behavior. Some popular natureinspired techniques based on swarm intelligence are—particle swarm optimization (PSO) [1], ant colony optimization (ACO) [2], artificial bee colony (ABC) algorithm [3], ant lion optimizer (ALO) [4], grey wolf optimizer (GWO) [5], and so on. These techniques have shown the great potential of swarm intelligence while solving the real-world optimization problems. In the litera
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