Since the search process of the particle swarm optimization (PSO) technique is non-linear and very complicated, it is hard if not impossible, to mathematically model the search process and dynamically adjust the PSO parameters. Thus, already some fuzzy systems proposed to control the important structural parameters of basic PSO. However, in those researches no effort were reported for optimizing the structural parameters of the designed fuzzy controller. In this paper, a new algorithm called Fuzzy Optimum PSO (FOPSO) has been introduced. FOPSO utilizes two optimized fuzzy systems for optimal controlling the main parameters of basic PSO. Extensive experimental results on many benchmark functions with different dimensions show that the powerfulness and effectiveness of the proposed FOPSO outperforms other versions of PSO.

Resilient modulus of subgrade soil is one of the most important parameters in terms of pavement analysis and design. This parameter is used for design of pavement structure based on both empirical (e.g. AASHTO 1993) and mechanistic-empirical methods (e.g. MEPDG). In order to determine resilient modulus, dynamic triaxial loading test should be conducted at different confining and deviator stresses on the soil samples and conducting such a test is very time consuming and costly. This paper aims to evaluate three hybrid neuro-computing methods including Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Support Vector Machine-Particle Swarm Optimization (SVM-PSO) and Adaptive neuro-Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO) for predicting resilient modulus of fine-grained soils. Input parameters in all of these models were considered as particles passing #200 sieve, liquid limit, plastic index, moisture content, optimum moisture content, degree of saturation, unconfined compression strength, confining stress, and deviator stress and output was assumed as resilient modulus of soil. Results show that ANN-PSO method has the highest accuracy in comparison with other methods. Coefficient of determination (R2) for ANN-PSO method was determined as 0.992 in case of overall dataset and in most cases the prediction error of resilient modulus using this method was less than 20%. Coefficient of determination for SVM-PSO method and ANFIS-PSO method were determined as 0.989 and 0.951, respectively. Results of this study also showed that the input parameter of particles passing #200 sieve has maximum influence on the resilient modulus of fine grained soil materials while the deviator stress has minimum impact on the resilient modulus of this type of materials.

Integration and diversity of IOT terminals and their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, and reliability of IOT is vital. Traditional intrusion detection technology has the disadvantages of low detection rates and weak scalability that cannot adapt to the complicated and changing environment of the Internet of Things. Hence, one of the most widely used traditional methods is the use of neural networks and also the use of evolutionary optimization algorithms to train neural networks can be an efficient and interesting method. Therefore, in this paper, we use the PSO algorithm to train the neural network and detect attacks and abnormalities of the IOT system. Although the PSO algorithm has many benefits, in some cases it may reduce population diversity, resulting in early convergence. Therefore, in order to solve this problem, we use the modified PSO algorithm with a new mutation operator, fuzzy systems and comparative equations. The proposed method was tested with CUP-KDD data set. The simulation results of the proposed model of this article show better performance and 99% detection accuracy in detecting different malicious attacks, such as DOS, R2L, U2R, and PROB.

The main objective of the current study is to utilize the capabilities of recently developed meta-heuristic algorithms for structural cost optimization of a one-way reinforced concrete ribbed slab simply supported at both ends. Two of these new and simple optimization algorithms, known as colliding bodies optimization (CBO) and democratic particle swarm optimization (DPSO), and a renowned optimization algorithm, PSO, are presented to solve cost optimization of a concrete ribbed slab. Although PSO is a very well-known and commonly used optimization algorithm, democratic PSO is an improved version of particle swarm optimization method. In DPSO the emphasis is placed upon improving the premature convergence phenomenon which is believed to be one of defects of the original PSO. CBO utilizes simple formulation to find optimum values and does not need any internal parameter. Performance of these algorithms is compared with harmony search. The results illustrate the power of the CBO and effectiveness of improvements of DPSO method in the present optimization problem.

An ideal traction and braking system not only ensures ride comfort and transportation safety but also attracts significant cost benefits through reduction of damaging processes in wheel-rail and optimum on-time operation. In order to overcome the problem of the wheel slip/slide at the wheel-rail contact surface, detection of adhesion and its changes has high importance and scientifically challenging, because adhesion is influenced by different factors. However, critical information this detection provides is applicable not only in the control of trains to avoid undesirable wear of the wheels/track but also the safety compromise of rail operations. The adhesion level between the wheel and rail cannot be measured directly but the friction on the rail surface can be measured using measurement techniques. Estimation of wheel-rail adhesion conditions during railway operations can characterize the braking and traction control system. This paper presents the particle swarm optimization (PSO) based Extended Kalman Filter (EKF) to estimate adhesion force. The main limitation in applying EKF to estimate states and parameters is that its optimality is critically dependent on the proper choice of the state and measurement noise covariance matrices. In order to overcome the mentioned difficulty, a new approach based on the use of the tuned EKF is proposed to estimate induction motor (as a main part of the train moving system) parameters. This approach consists of two steps: In the first step the covariance matrices are optimized by PSO and then, their values will be introduced in the estimation loop. .