Advances, Applications & Challenges in Metaheuristics: In Celebration of the Cuckoo Search Algorithm

Cuckoo Search, a nature inspired optimization algorithm, was invented by Xin-She Yang (UK) & Suash Deb (India) 10 years ago. Since then it had been proved to be an important & a very useful tool for addressing myriads of optimization problems, both for industrial applications as well as for theoretical ones. Recently the 1st publication of Cuckoo search, published in the year 2009, reached 5000 citations (Google Scholar). In order to celebrate these milestones, SN Operations Research Forum intends to publish a special topical collection by assembling a collection of papers and showcasing the recent efforts in the areas of metaheuristics and nature inspired computing. Accordingly, the journal invites manuscripts, which neither had been published already nor submitted for consideration elsewhere, for this special issue highlighting “Advances, Applications & Challenges in Metaheuristics” to be guest edited by Ka-Chun Wong (Hong Kong) & Thomas Hanne (Switzerland) & due for publication in 2021. The aim here is to present academicians/researchers/industry professionals/post graduate students some of state-of-the-art research on metaheuristics and the cuckoo algorithm through a cluster of original research contributions in any aspect of the below areas as well as their novel applications. Articles highlighting the challenges as faced by the researchers and/or envisaged by them as well as comprehensive surveys are also welcome in the aforesaid areas. Along with Cuckoo Search (CS) and the other well-known metaheuristics algorithms like Particle swarm optimization (PSO), Ant colony optimization (ACO), Memetic algorithms, Evolutionary algorithms, Simulated annealing, Artificial bee colony etc., articles dealing with the following (but not limited to) areas are also solicited: • Artificial immune systems • Chaos optimization • Central Force Optimization • Chicken Swarm Optimization • Earthworm Optimization • Elephant Herding Optimization • Firefly algorithm • Grey wolf optimization • Harmony search • Monarch butterfly optimization • Rainfall optimization • Sperm Swarm optimization • Tabu search • Wolf search optimization • Whale optimization


  • Ka-Chun Wong

    Dept. of Computer Science, City University of Hong Kong, Hong Kong (

  • Thomas Hanne

    Inst. of Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Switzerland (


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