Some Words About Nature-Inspired Computing

The use of artificial intelligence (AI) in various domains has drastically increased during the last decade. Nature-inspired computing is a strong computing approach that belongs to AI and covers a wide range of techniques. It has successfully tackled many complex problems and outperformed several classical techniques. This chapter provides the original ideas behind some nature-inspired computing techniques and their applications, such as the genetic algorithms, particle swarm optimization, grey wolf optimizer, ant colony optimization, plant propagation algorithm, cuckoo optimization algorithm, and artificial neural networks.

Using the Cuckoo Optimization Algorithm to Solve the Point Coverage Problem with Moving Targets in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been used in many sectors in recent years. Easy deployment and low prices are the main reasons for using WSN. On the other hand, power source limitations and unstructured overlay networks are the major concerns in these kinds of networks. Region coverage is one of the problems that should be solved smartly in order to maximize the productivity of the network. Appropriate sensor selection and efficient energy usage are essential in region coverage. Pointwise coverage is a well-known version of the coverage problem. Since this is an NP-complete problem, many approaches have already been designed to solve it. The main shortcoming in previous works is the short network lifetime due to high energy consumption. In this paper, an enhanced method has been proposed based on the Cuckoo Optimization Algorithm (COA). By means of an adjusted version of the cuckoo search and a heuristic fitness function, it has been possible to expand the lifespan of the network. The proposed algorithm is composed of three phases: the first phase is setting up, the second phase is the selection, and the third phase is stable status. Simulation results show that the proposed method in comparison with recent works has achieved a sensible optimization in energy consumption, lifetime, and coverage quality.

Cuckoo optimization algorithm in reverse logistics: A network design for COVID-19 waste management

Today, reverse logistics (RL) is one of the main activities of supply chain management that covers all physical activities associated with return products (such as collection, recovery, recycling and destruction). In this regard, the designing and proper implementation of RL, in addition to increasing the level of customer satisfaction, reduces inventory and transportation costs. In this paper, in order to minimize the costs associated with fixed costs, material flow costs, and the costs of building potential centres, a complex integer linear programming model for an integrated direct logistics and RL network design is presented. Due to the outbreak of the ongoing global coronavirus pandemic (COVID-19) at the beginning of 2020 and the consequent increase in medical waste, the need for an inverse logistics system to manage waste is strongly felt. Also, due to the worldwide vaccination in the near future, this waste will increase even more and careful management must be done in this regard. For this purpose, the proposed RL model in the field of COVID-19 waste management and especially vaccine waste has been designed. The network consists of three parts – factory, consumers’ and recycling centres – each of which has different sub-parts. Finally, the proposed model is solved using the cuckoo optimization algorithm, which is one of the newest and most powerful meta-heuristic algorithms, and the computational results are presented along with its sensitivity analysis.