The recent global health crisis also known as the COVID-19 or coronavirus pandemic has attracted the researchers’ attentions to a treatment approach called immune plasma or convalescent plasma once more again. The main idea lying behind the immune plasma treatment is transferring the antibody rich part of the blood taken from the patients who are recovered previously to the critical individuals and its efficiency has been proven by successfully using against great influenza of 1918, H1N1 flu, MERS, SARS and Ebola. In this study, we modeled the mentioned treatment approach and introduced a new meta-heuristic called Immune Plasma (IP) algorithm. The performance of the IP algorithm was investigated in detail and then compared with some of the classical and state-of-art meta-heuristics by solving a set of numerical benchmark problems. Moreover, the capabilities of the IP algorithm were also analyzed over complex engineering optimization problems related with the noise minimization of the electro-encephalography signal measurements. The results of the experimental studies showed that the IP algorithm is capable of obtaining better solutions for the vast majority of the test problems compared to other commonly used meta-heuristic algorithms.

The immune plasma algorithm (IP algorithm or IPA) is one of the most recent meta-heuristic techniques and models the fundamental steps of immune or convalescent plasma treatment, attracting researchers’ attention once more with the COVID-19 pandemic. The IP algorithm determines the number of donors and the number of receivers when two specific control parameters are initialized and protects their values until the end of termination. However, determining which values are appropriate for the control parameters by adjusting the number of donors and receivers and guessing how they interact with each other are difficult tasks. In this study, we attempted to determine the number of plasma donors and receivers with an improved mechanism that depended on dividing the whole population into two sub-populations using a statistical measure known as the percentile and then a novel variant of the IPA called the percentile IPA (pIPA) was introduced. To investigate the performance of the pIPA, 22 numerical benchmark problems were solved by assigning different values to the control parameters of the algorithm. Moreover, two complex engineering problems, one of which required the filtering of noise from the recorded signal and the other the path planning of an unmanned aerial vehicle, were solved by the pIPA. Experimental studies showed that the percentile-based donor–receiver selection mechanism significantly contributed to the solving capabilities of the pIPA and helped it outperform well-known and state-of-art meta-heuristic algorithms.

The problem of inefficient and unevenly distributed spectrum usage in industrial wireless networks has led to the emergence of the concept of industrial cognitive radio (CR) networks, which have particularly important applications in automotive industry. Industrial CR networks are planned to function in a wide spectrum range; therefore, they have high energy consumption because of frequency switching while other wireless technologies do not have this problem. A distinctive feature of this switching cost is that it depends on the wideness between the two frequency bands. In this article, we formulate the joint frequency assignment and scheduling problem for multihop industrial CR networks with a single transceiver by considering varying amounts of energy consumption that occurs while CR devices switch to different frequency bands. Our optimization problem, which we formulate as an integer linear program, minimizes the energy cost related to frequency switching while making frequency and time slot allocation to the cognitive devices. We prove that even on star graphs, our formulated problem is inapproximable within any polynomial-time computable function f(n) in addition to being NP-Hard in the strong sense. Therefore, we propose a polynomial-time heuristic algorithm to solve the energy consumption problem due to channel switching. Simulation results demonstrate that the performance of our heuristic algorithm is very close to the results obtained from the integer linear programming implementation by CPLEX optimization software. We also compare our proposed method with the corresponding constant energy consumption for frequency switching case and two state-of-the-art algorithms and demonstrate that taking into account the different energy consumption while switching to different frequency bands is vital for joint frequency assignment and scheduling in multihop industrial CR networks with a single transceiver.

Sensor node deployment is very crucial for wireless sensor networks (WSNs). Sensors' coverage plays an integral role in all types of applications that utilize WSNs. In addition to maximizing the life of the network and consuming less energy, the better positioning of the sensors makes the sensors more effective and efficient. A meta-heuristic algorithm will be used to determine where wireless sensors should be placed to maximize the coverage of the area where they are deployed. As a part of our solution, we will utilize the Immune Plasma Algorithm (IPA), which transfers plasma or the antibody-rich part of the blood from one patient to another through a process of bypassing the immune system. This optimization algorithm will be used to determine the optimal solution to the maximum coverage problem. The results of the simulation will be compared with solutions found by one research using Artificial Bee Colony (ABC), an algorithm that models the foraging behavior of bees.

Internet of Things (IoT) is a network that includes smart objects. IoT is generated concept which helps to connect our daily objects, making our devices smarter, thinking without any human hand. There are a lot of advantages of this concept in our life. IoT has become a significant subject to several researchers by its applications and challenges. Wireless Sensor Networks (WSNs) can be considered that element of IoT frame. WSNs structure includes specific sensor group that working independent, publishing and sharing detected data. IoT and WSN are using in various areas such as military, daily use, transportation. Although numerous advantages and using fields, these networks have some challenges. Routing and transmitting of data is one of these challenges. Maximize network lifetime, packet delivery ratio, minimize delay, link failures and optimize reliability during routing is one of the major objectives. Quality of Service (QoS) is also includes these objectives. There are various works for routing and path selection. In this paper, we reviewed routing algorithms and models in terms of success criterions such as minimize delay, energy consumption and maximize data delivery ratio. We divided IoT and IoT based WSN algorithms into two cagetories to classify: energy aware and delay, throughput, data delivery, packet loss aware. We presented a table which includes these criterions.