Comparative Analysis Among Five Stochastic Search-Based Optimization Algorithms

The primary user emulation attack (puea) is considered to be one of the common threats in cognitive radio networks (crns). in this problem, an attacker emulates the primary user (pu) signal to deceive other secondary users (sus) and forcing them to leave the white spaces (free spaces) in the spectrum assigned before to the PU. The PUEA is detected and localized using the time-difference-of-arrival (TDOA) localization technique based on stochastic optimization algorithms. Particle swarm optimization (PSO) algorithms are proposed to minimize the cost function provided by the TDOA measurements and to increase the accuracy of the detection. The PSO variants are evolved by changing the parameters of the standard PSO such as inertia weight and acceleration constants. These approaches are presented and compared with the standard PSO in terms of convergence speed and processing time. This paper presents the first study of designing a PSO algorithm suitable for the localization problem and it will be considered as a good guidance for applying the optimization algorithms in wireless positioning techniques. Mean square error (MSE) and cumulative distribution function (CDF) are used as the evaluation metrics to measure the accuracy of the proposed algorithms. Simulation results show that the proposed PSO approaches provide higher accuracy and faster convergence than the standard PSO, social spider optimization (SSO), cuckoo search (CS), firefly optimization (FA) and Taylor series estimation (TSE) methods.

Operation optimization of natural gas pipelines has received increasing attentions, due to such advantages as maximizing the operating economic benefit and the gas delivery amount. This paper provides a review on the most relevant research progress related to the steady-state operation optimization models of natural gas pipelines as well as corresponding solution methods based on stochastic optimization algorithms. The existing operation optimization model of the natural gas pipeline is a mixed-integer nonlinear programming (MINLP) model involving a nonconvex feasible region and mixing of continuous, discrete, and integer optimization variables, which represents an extremely difficult problem to be solved by use of optimization algorithms. A survey on the state of the art demonstrates that many stochastic algorithms show better performance of solving such optimization models due to their advantages of handling discrete variables and of high computation efficiency over classical deterministic optimization algorithms. The essential progress mainly with regard to the applications of the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA) algorithms, and their extensions is summarized. The performances of these algorithms are compared in terms of the quality of optimization results and the computation efficiency. Furthermore, the research challenges of improving the optimization model, enhancing the stochastic algorithms, developing an online optimization technology, researching the transient optimization, and studying operation optimization of the integrated energy network are discussed.

Density functional theory (DFT) is an efficient instrument for describing a wide range of nanoscale phenomena: wetting transition, capillary condensation, adsorption, etc. In this paper, we suggest a method for obtaining the equilibrium molecular fluid density in a nanopore using DFT without calculating the free-energy variation─Variation-Free Density Functional Theory (VF-DFT). This technique can be used to explore confined fluids with a complex type of interactions, additional constraints, and, to speed up calculations, which might be crucial in an inverse problem. The fluid density in VF-DFT approach is represented as a decomposition over a limited set of basis functions. We applied principal component analysis (PCA) to extract the basic patterns from the density function and take them into account in the construction of a set of basis functions. The decomposition coefficients of the fluid density by the basis were sought by stochastic optimization algorithms: genetic algorithm (GA) and particle swarm optimization (PSO), to minimize the free energy of the system. In this work, two different fluids were studied: nitrogen at a temperature of 77.4 K and argon 87.3 K, at a pore size of 3.6 nm, and the performance of optimization algorithms was compared. We also introduce the Hybrid Density Functional Theory (H-DFT) approach based on stochastic optimization methods and the classical Picard iteration method to find the equilibrium fluid density starting from the physically appropriate solution. The combination of Picard iteration and stochastic algorithms helps to significantly speed up the calculations of equilibrium density in the system without losing the quality of the solution, especially in cases with the high relative pressure and expressed layering structure.

Waveform-based microseismic location using stochastic optimization algorithms: A parameter tuning workflow

In this paper, the novel hybrid enhanced particle swarm optimization and differential evolution (EPSO/DE) optimization technique [1–2] is used in designing different configurations of concentric circular arrays (CCAA) and concentric hexagonal arrays (CHAA). Many advantages were achieved by the rearrangement of the circular arrays (CA) and hexagonal arrays (HA) elements into CCAA and CHAA multi-ring geometries. Different configurations with two-rings, three-rings and four-rings are designed with the same number of elements and the same main beamwidth of the initial circular and helical geometries. The objective of this paper is to design optimum multi-ring antenna arrays that achieve minimum sidelobe level (SLL), high directivity and reduced mutual coupling in compare to traditional circular and helical arrays. Hybrid EPSO/DE algorithm is applied to optimize the complex weights (amplitude and phase) of the elements current. The objective is to minimize the SLL of the concentric configuration arrays while achieving the highest possible directivity and minimum mutual coupling.

Particle Swarm Optimization or Differential Evolution—A comparison

Application of fast analytical approach and AI optimization techniques to hydraulic fracture stage placement in shale gas reservoirs

Electric vehicle penetration in the transport section is increasing and replacing the conventional fossil fuel based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location in the electrical network affects the energy loss and voltage deviation. Therefore, a road network of urban area overlapped with a 33-bus distribution network has been taken as test system for this work. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation have been attempted simultaneously using 2-layer optimization. Differential Evolution and Harris Hawks Optimization techniques have been employed to solve the problem and the final results have been validated using eight other established optimization techniques. 2m point estimation method has been used to take care of uncertainties related to electric vehicles and PV. Monte-carlo simulation is also applied to cross verify the performance. The land cost, customer accessibility to charging station have been taken into account to allocate it at proper places. The whole work has been performed based on the 24-hrs dynamically varying electric vehicles flow and PV outputs.

Allocation of electric vehicle charging station considering uncertainties

The practical economic dispatch (ED) problems have many non-convex characteristics, which makes the searching of the global optimum difficult when using traditional mathematical methods. This paper presents a novel hybrid algorithm (HA) to the ED problems based on the particle swarm optimization (PSO) technique and differential evolution (DE) algorithm. Since the standard PSO has the adversity of premature convergence, the mutation and crossover operators of the DE, as well as the chaotic sequences, are considered to be integrated into the PSO to improve the global searching ability. Moreover, the dynamic penalty function is applied to deal with the constraints. The proposed method is tested on a 15-unit system considering the ramp rate limits, operating zones, and network losses. Also, the results are compared with those of other optimization methods.

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques

This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics—differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.

This contribution deals with identification of fractional-order dynamical systems. System identification, which refers to estimation of process parameters, is a necessity in control theory. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme of estimating the parameters for such a fractional order process is proposed. This method employs fractional calculus theory to find equations relating the parameters that are to be estimated, and then estimates the process parameters after solving the simultaneous equations. The said simultaneous equations are generated and updated using particle swarm optimization (PSO) technique, the fitness function being the sum of squared deviations from the actual set of observations. The data used for the calculations are intentionally corrupted to simulate real-life conditions. Results show that the proposed scheme offers a very high degree of accuracy even for erroneous data.

Summary The efficiency of location methods is crucial for real-time microseismic monitoring. Migration-based methods can detect and locate very weak microseismic events, but they generally require more processing time and memory space than traveltime inversion. We propose to improve the efficiency of microseismic imaging methods with the particle swarm optimization algorithm, which is a stochastic population-based nonlinear optimization algorithm that can rapidly converge toward the optimum. In this work, we utilize the diffraction stacking operator and the cross-correlation stacking operator with the particle swarm optimization algorithm. The results of numerical and field data examples show that the proposed method can accelerate the imaging process without requiring additional memory space.