These computational techniques draw their basis from the natural phenomena to solve complex real-world problems in various areas of computer applications. Most of the nature-inspired algorithms are meta-heuristic and capable of handling NP-hard computing problems. They combine both randomness and rules while imitating some natural phenomena and basic principles of physics, chemistry and biology. Based on the source of inspiration, nature-inspired algorithms can be broadly classified into physics-based, chemistry-based and biology-based. Simulated Annealing (SA), which draws its basis from principles of thermodynamics, is a classic case of physics-based nature-inspired algorithm used in search and optimization problems. Chemical Reaction Optimization (CRO) is a chemistry-based nature-inspired algorithm that follows the principles of chemical reaction. Similarly, Artificial Neural Network (ANN) based on biological neurons of human brain is another representative of nature-inspired algorithms.Although nature-inspired algorithms are not new, many of these algorithms have been developed in recent years, triggering a new area of research and investigation. Particularly, biology-based algorithms have been attracting a good deal of research attention due to their satisfactory performance in many areas of application. Techniques such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Cuckoo Search (CS) are quite popular. Many of these algorithms are based on the interesting collective behavior of birds, animals and insects while they live or move in groups. Limited intelligence of these organisms provides enough motivation to develop computational algorithms which are very efficient to solve complex computational problems. Despite their popularity, these algorithms face many challenges which attract further research efforts.

The first paper, “A Novel Approach to Overcoming Sample Impoverishment Problem of Particle Filter Using Chaotic Crow Search Algorithm”, by Ashish Kumar, Gurjit Singh Walia and Kapil Sharma, proposes a technique to solve the problem of sample impoverishment and particle degeneracy of particle filter method. Based on the simulated data, the authors have shown that their approach outperforms other existing approaches and delivers satisfactory results.

The second paper, “Relationship Between Acceptance of Virtual Private Cloud (VPC) and Adoption of VPC: An Empirical Study”, by J Webb and O Aly, investigates the relationship between acceptance and adoption of VPC solutions. The authors have used several statistical techniques to process collected data and interpret the results. The results indicate that security is not of much concern while adopting VPC solutions, as there is trust in the VPC solution to mitigate security threats.

The last paper, “Roundabouts Modeling and Vehicular Traffic Control Techniques: A Survey”, by Babangida Zachariah, Philip O Odion and Rambo I Saidu, presents a survey of approaches used in modeling and controlling vehicular traffic flow at a roundabout. The authors have considered several aspects such as simulation and optimization tools and techniques used, number of lanes, entry and exit points, vehicle type, control approach, etc. The survey reveals that multi-lane and coordinated roundabouts, which are more challenging to design and represent next generation road networks, are not given due attention.