The development and application of photovoltaic (PV) systems are becoming increasingly more important as the global need for energy resources expands and environmental protection becomes more highly valued. Parameters of PV models can be identified by measuring their current–voltage ( I – V ) characteristic curves. Identifying these parameters quickly, accurately and reliably is critical in determining the operating status of in situ PV arrays and, in turn, optimising solar energy conversions. To achieve both fast and accurate parameter identification with high reliability, a new algorithm called algorithm based on SSO and Nelder–Mead simplex (NMS) (SSSO) based on the simplified swarm optimisation (SSO) and the NMS is proposed in this study. To demonstrate the performance of SSSO in identifying solar cell system parameters, its performance on the single diode model and the double diode model was compared with existing algorithms in terms of both fitness value and run time. The experiment results indicate that SSSO outperformed the compared algorithms in both run time and standard deviation of fitness value.

Wireless (smart) sensor networks (WSNs) comprise a myriad of embedded wireless smart sensors. They play a cardinal role in the functioning of many applications, such as the Internet of Things, smart grids, smart production systems, and smart homes, which ultimately render them paramount instruments in the modern age. Recent advances in WSNs have resulted in the rapid development of sensors. However, WSNs will only able to achieve better execution efficiencies if their energy consumption - owing to limited battery life and difficulty of recharging - can be better controlled. Moreover, signal transmission quality determines WSN performance. Hence, two main concerns - energy consumption and signal transmission quality - should be addressed to improve the performance of WSNs. Thus, a new bi-objective simplified swarm optimization algorithm (bSSO) is proposed by employing the concepts of simple routing, SSO, and crowd distance. The performance and applicability of the proposed bSSO using eight different parameter settings are demonstrated through an experiment involving ten WSN benchmarks ranging from 100 to 1000 sensors. The proposed algorithm is then compared with NSGA-II, which is an algorithm widely used to solve multi-objective problems. The results show that the proposed bSSO can successfully achieve the aim of this work.

Wireless (smart) sensor networks (WSNs), networks made up of embedded wireless smart sensors, are an important paradigm with a wide range of applications, including the internet of things (IoT), smart grids, smart production systems, smart buildings and many others. WSNs achieve better execution efficiency if their energy consumption can be better controlled, because their component sensors are either difficult or impossible to recharge, and have a finite battery life. In addition, transmission cost must be minimized, and signal transmission quantity must be maximized to improve WSN performance. Thus, a multi-objective involving energy consumption, cost and signal transmission quantity in WSNs needs to be studied. Energy consumption, cost and signal transmission quantity usually have uncertain characteristics, and can often be represented by fuzzy numbers. Therefore, this work suggests a fuzzy simplified swarm optimization algorithm (fSSO) to resolve the multi-objective optimization problem consisting of energy consumption, cost and signal transmission quantity of the transmission process in WSNs under uncertainty. Finally, an experiment of ten benchmarks from smaller to larger scale WSNs is conducted to demonstrate the effectiveness and efficiency of the proposed fSSO algorithm.

This study aims to develop a novel credibility-based fuzzy model for the constraints of a series-parallel redundancy allocation problem (RAP) with a mix of components under an uncertainty environment and investigates the reliability enhancement of wireless sensor networks (WSN) through a RAP for objective system reliability by subjecting it to two nonlinear constraints, i.e., cost and weight constraints, under an uncertainty environment. In this work, fuzzy numbers have been adopted to overcome the problems related to the exact values of cost and weight of components used in system that are hard to obtain due to uncertainty in the real world. Next, the credibility theory is utilized to convert the fuzzy numbers to crisp numbers. In addition, the difficulty of the RAP encountered is the need to resiliently sustain the two nonlinear constraints, while also aiming to maximize system reliability by optimizing the redundancy allocation of components in parallel of each subsystem in the WSN. Hence, the model is solved by an improved simplified swarm optimization (ISSO). To prove the effectiveness of the ISSO in solving the researching model, experimental results are tested on random benchmarks. The experimental results show a significant efficiency and effectiveness of the proposed ISSO.

Social networks have increasingly become important and popular in modern times. Moreover, the influence of social networks plays a vital role in various organizations, including government organizations, academic research organizations and corporate organizations. Therefore, strategizing the optimal propagation strategy in social networks has also become more important. Increasing the precision of evaluating the propagation probability of social networks can indirectly influence the investment of cost, manpower and time for information propagation to achieve the best return. This study proposes a new algorithm, which includes a scale-free network, Barabási–Albert model, binary-addition tree (BAT) algorithm, PageRank algorithm, Personalized PageRank algorithm and a new BAT algorithm to calculate the propagation probability of social networks. The results obtained after implementing the simulation experiment of social network models show that the studied model and the proposed algorithm provide an effective method to increase the efficiency of information propagation in social networks. In this way, the maximum propagation efficiency is achieved with the minimum investment.