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This paper addresses optimization problems subject to an objective function's evaluation‐number constraint arbitrarily given in advance. These problems are practical for black‐box objective functions which cost much money and/or time per evaluation because money and/or time constraints are typically applicable in real‐world projects. This paper suggests a new solution strategy that can be used to adapt metaheuristics to evaluation‐number constraints arbitrarily given in advance. The proposed strategy updates the setting parameters of a target metaheuristic algorithm for every generation by solving a fixed small‐size optimization problem related to the evaluation‐number constraint. It has the advantage of being insensitive to the effects of dimensionality increases in principle. The effectiveness of the proposed strategy is confirmed by numerical experiments with two well‐known metaheuristics, i.e., the particle swarm optimization algorithm and the differential evolution algorithm. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Vehicular Adhoc Networks (VANET) facilitate inter‐vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing‐based Secure Protocol for VANET Environment (SC‐SPVE) method, which aims to tackle security challenges. The SC‐SPVE technique integrates an adaptive neuro‐fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC‐SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2. [ABSTRACT FROM AUTHOR]

The regulation and stabilization of a system's output to achieve desired performance and ensure system reliability require the use of different controllers, but selecting appropriate control parameters presents a challenge in ensuring robustness and stability. Proportional–integral–derivative (PID) controllers are popularly used due to their simplicity and effectiveness in addressing these challenges. In this paper, a simple, effective, and efficient, novel graphical technique is proposed to design PID and its variants (PI/PD) controllers, which addresses the challenges in selecting appropriate control parameters. The method involves creating three equispaced vectors for controller parameters (Kp,Ki,Kd)$(K_{\text{p}}, K_{\text{i}}, K_{\text{d}})$, and obtaining a 3D (2D in PI/PD) Cartesian grid of these vectors. All nodes in the grid provide several possible controllers, and integral time squared error (ITSE) is calculated for each controller from the closed‐loop step response of the system. The obtained ITSE is plotted in a 4D (PID) or 3D (PI/PD) graph, and controller parameters corresponding to the minimum value of ITSE are identified. Furthermore, the proposed graphical technique aids in choosing the lower and upper bounds (LB and UB) if the controller is designed using optimization techniques. The better selection of LB and UB reduces the search space resulting in lesser execution time and fewer iterations. To validate the proposed graphical technique, we designed various controllers for widely‐used brush‐less DC and switch reluctance motors in electric vehicles. Additionally, by choosing the LB and UB with the proposed technique, controllers are also designed using three optimization techniques: particle swarm optimization, black widow optimization algorithm, and honey badger algorithm. The obtained controllers using the graphical technique outperformed the optimization techniques in terms of time and frequency domain specifications, and the proposed selection of lower and upper bounds resulted in improved performance in terms of iterations and execution time. [ABSTRACT FROM AUTHOR]

Bio‐inspired algorithms have become popular due to their capability of finding good solutions for complex optimization problems in an acceptable computational time. The Grey Wolf Optimizer is a nature‐inspired, population‐based metaheuristic that simulates the social hierarchy and the hunting strategy observed in a grey wolf pack. Although the Grey Wolf Optimizer has been successfully applied to solve different optimization problems, it may suffer from premature convergence and get stuck in local optima. In order to overcome these drawbacks, this paper proposes a chaotic version of the Grey Wolf Optimizer that differs from the original algorithm and previously published modified versions because it does not add a chaotic variable in the parameters that control the execution of the algorithm. Instead, the proposed model uses a chaotic variable to define the wolves in the pack that will be used to guide the hunting process in each iteration of the algorithm. Numerical experiments using 20 benchmark functions are carried out. The performance of the proposed model is compared with the performance of the original Grey Wolf Optimizer and other well‐known algorithms, namely the Particle Swarm Optimization, the Genetic Algorithm, the Symbiotic Organisms Search, and the Teaching‐Learning Based Optimization. Nine chaotic maps reported in the literature are tested. The results show that the proposed algorithm has a very competitive performance, and the Chebyshev map presented the best performance among the chaotic maps simulated. The proposed algorithm can be integrated into other modified versions of the Grey Wolf Optimizer in a straightforward way. [ABSTRACT FROM AUTHOR]

In this paper, tensile properties of polylactic acid/polyolefin elastomer/multiwall carbon nanotubes/carbon black particles (PLA/POE/MWCNT/CB) nanocomposites were investigated. Nanocomposites, including each nano‐filler at 0, 0.5,1, 1.5, 2, and 3 wt% levels were made by an internal mixer. Tensile tests were carried out to extract the tensile properties of samples. Field emission electron microscopy (FESEM) was also performed to observe the dispersion quality of nano‐fillers in the matrix. FESEM images indicate a proper dispersion of all loadings of nanotubes in the matrix, in contrast the presence of more than 2 wt% of carbon black into the matrix is associated with their agglomeration. Based on the tensile tests, the separate and simultaneous addition of nano‐fillers into binary matrix leads to an increase in strength, modulus, and elongation by 72%, 30%, and 22%, respectively. Furthermore, predicted data from the rules section of the fuzzy inference system (FIS) method with the lowest error led to extracting regression models with R2 above 90% for all responses. Finally, the particle swarm optimization (PSO) method showed that to achieve balancing between the strength and elongation at break the nano‐fillers must be set at 0.64 wt% for nanotubes and 0.94 wt% for carbon black particles. [ABSTRACT FROM AUTHOR]

Electricity access is a critical factor for social and economic development. Microgrid becomes cost‐effective means to provide access to electricity for the rural community. Rural electrification demands high investment due to its low energy demand and population. Planning of rural electrification is a technical and financial challenge for policymakers. This paper proposes the grid‐connected electrification of the rural community of Uttarakhand (India). An integrated model formed with the solar photovoltaic system, wind energy, and battery. This research aims to provide the optimal cost‐effective solution for the electrification of the rural community. The suggested model is optimized using Differential Algorithm to determine the optimal system configuration, cost of energy, and net present cost. The effectiveness of the proposed DE algorithm is compared with genetic algorithm and particle swarm optimization. The results confirm that the proposed renewable energy‐based microgrid has a total production cost of $5 185 367.04, with a per‐unit cost of energy is $0.20/kWh. The proposed microgrid is a more suitable option for electrification in rural communities with less dependence on the grid and conventional sources. [ABSTRACT FROM AUTHOR]

As distribution networks operate in their majority in a radial way, this condition becomes one of the most important constraints of distribution network reconfiguration (DNR) problem. There are diverse ways to represent this in DNR, but a lack of studies that provide an analysis of its behavior is noted, specially regarding its influence in metaheuristics and bio‐inspired metaheuristics. This paper presents a discussion between two alternatives to represent this in DNR in conjunction with three new bio‐inspired metaheuristics. One is based on the formation of the incidence matrix of the system and its determinant; the other is a modified approach of a method that establishes a set of forbidden switches through the analysis of the system fundamental loops. To solve DNR, three new bio‐inspired metaheuristics are presented: monarch butterfly optimization (MBO), gray wolf optimizer (GWO), and marine predators algorithm (MPA). A comparison is performed with selective particle swarm optimization (SPSO). To test the alternatives, three systems are used (33‐, 69‐, and 84‐ bus), with results showing the influence of the radiality approach in the results of bio‐inspired metaheuristics. [ABSTRACT FROM AUTHOR]

The objective of moveout parameter inversion is to derive sets of parameter models that can be used for moveout correction and stacking at each common midpoint location to increase the signal‐to‐noise ratio of the data and to provide insights into the kinematic characteristics of the data amongst other things. In this paper, we introduce a data‐driven user‐constrained optimization scheme that utilizes manual picks at a point on each reflector within a common midpoint gather to constrain the search space in which an optimization procedure can search for the optimal parameter sets at each reflection. The picks are used to create boundary curves which can be derived approximately via an optimization technique or analytically via the derivation of an analytical bounds function. In this paper, we derive analytical forms of bounds functions for four different moveout cases. These are normal moveout, non‐hyperbolic moveout, azimuthally dependent normal moveout and azimuthally dependent non‐hyperbolic moveout. The optimization procedure utilized here to search for the optimal moveout parameters is the particle swarm optimization technique. However, any metaheuristic optimization procedure could be modified to account for the constraints introduced in this paper. The technique is tested on two‐layer synthetic models based on three of the four moveout cases discussed in this paper. It is also applied to an elastic forward modelled synthetic model called the HESS model, and finally to real 2D land data from Alaska. The resultant stacks show a marked improvement in the signal‐to‐noise ratio compared to the raw stacks. The results for the normal moveout, non‐hyperbolic moveout and azimuthally dependent normal moveout tests suggest that the method is viable for said models. Results demonstrate that our method offers potential as an alternative to conventional parameter picking and inversion schemes, particularly for some cases where the number of parameters in the moveout approximation is 2 or greater. [ABSTRACT FROM AUTHOR]

Summary: The emerging network including long‐term evolution‐advanced (LTE‐A) aims at enhancing the telecommunication customer satisfaction in numerous aspects including system capacity, network coverage, handover management, and quality of service (QoS). Effective handover (HO) management reduces HO failure and hence enhances the data rate and supports user mobility. There are numerous challenges that increase the call drop rate. Among the main challenges that increase the call drop rate, the variable user speeds and variable traffic loads are the major ones. Hence, the time to trigger (TTT), HO margin (HOM), and HO offset (HOO) are used to evaluate the HO management using self‐organized network‐based heuristic algorithm under variable user speeds and variable traffic loads. In recent researches, different HO management techniques were applied to manage the HO decision including fuzzy‐logic tactics and Q‐learning. However, they did not apply intelligent optimization techniques that adapt the variable user speeds and dynamic traffic loads. This paper aims at increasing the telecom customer satisfaction by decreasing the call drop rate using particle swarm optimization (PSO), which adaptively manages the handover control parameters according to the user speed and traffic loads. The simulation results have shown that the proposed optimization tool results in significant call drop rate reduction compared to the ordinary HO management. [ABSTRACT FROM AUTHOR]

Most of the crude oil produced in China is highly viscous and easy to condense, and the pipeline transportation method is mostly heated. At present, frequency conversion technology is gradually applied to oil pump units. In order to find the best pipeline operation scheme, this paper is based on MATLAB programming software and established a corresponding mathematical model of oil pipeline energy consumption. This model innovatively takes the rotational speed of the variable frequency pump as one of the model optimization variables, calculating the pressure change and temperature change between stations of the pipeline, predicting the energy consumption of the pipeline and can guide the optimized operation of the pipeline, which has a high popularization and application value. An example of Qingtie Fourth‐line crude oil pipeline shows that among the genetic algorithm, particle swarm optimization, and simulated annealing algorithms for solving the optimization model, the particle swarm optimization algorithm has fast convergence speed, short optimization time, obtains the most optimal scheme, and can reduce energy consumption by 7.84%, which proves the practicability and creativity of the optimization model. [ABSTRACT FROM AUTHOR]

In this paper, the efficient amounts of radiation received by a flat‐plate collector and top heat loss coefficient along with affecting parameters are investigated using measured data in different climates of Iran. Coded program in MATLAB software is then distributed to calculate the optimum tilt angle for extracting the highest solar radiation in different months of the year for five cities of Iran with different geographic latitudes. An optimal annual slope is obtained for maximum utilization and for conditions where the collector can only be adjusted at an angle during the year. Using an interpolation, an equation has been derived for obtaining the optimal tilt angle of collector for areas with latitude between 27° and 37°. Inasmuch as the top heat loss coefficient is required for evaluating the thermal performance of solar collectors, the value for different temperatures is obtained at ambient temperature up to 200°C for the absorber plate. Effects of key parameters such as collector slope, number of covers, wind speed, emittance coefficient of adsorption plate, and cover spacing on top heat loss coefficient are investigated. In the end, the particle swarm optimization (PSO) method is employed to minimize the top heat loss coefficient. The minimum amount of top heat loss coefficient is obtained with the amount of 0.99 W/m2K. [ABSTRACT FROM AUTHOR]

In this paper, a modified time‐varying particle swarm optimization (MTVPSO) is proposed for solving nonconvex economic load dispatch problems. It is a variant of the traditional particle swarm optimization (PSO) algorithm. In an MTVPSO, novel acceleration coefficients for cognitive and social components are presented as linear time‐varying parameters in the velocity update equation of the PSO algorithm. In the early stages of the optimization process, it improves the global search capability of particles and directs the global optima at the end stage. Additionally, a linearly decreased inertia weight is introduced in an MTVPSO, instead of a fixed constant value, which helps improve the diversity of the population. Through this modification mechanism in PSO, the proposed algorithm has a higher probability of avoiding local optima, and it is likely to find global optima more quickly. Six complex benchmark functions have been used to validate the effectiveness of the proposed algorithm. Furthermore, to demonstrate its efficiency, feasibility, and fastness, six different cases (3‐, 6‐, 13‐, 15‐, and 40‐unit systems and one large‐scale Korean power 140‐unit system) of the economic load dispatch problem are solved by an MTVPSO. The results of the proposed algorithm have been compared with state‐of‐the‐art algorithms. It was found that the proposed MTVPSO can deliver better results in terms of solution quality, convergence characteristics, and robustness. [ABSTRACT FROM AUTHOR]

Voltage stability is very important, as without it voltage collapse will occur. There is therefore a need to incorporate the consideration of voltage stability into the optimal power flow (OPF). This paper presents a comparison of the effectiveness of five voltage stability indices (VSIs) when incorporated in an OPF. The improved particle swarm optimization (IPSO) algorithm is used to solve the OPF problem. The five VSIs investigated are the L‐index, fast voltage stability index (FVSI), line stability index (Lmn), online voltage stability index (LVSI), and voltage collapse proximity indicator (VCPI). The effectiveness of each VSI is considered in the terms of the generation cost, emission, transmission loss, and voltage stability. The IEEE standard 14‐, 30‐, 57‐, and 118‐bus test systems are used for the comparison. It is found that considering each voltage stability index as part of the objective function for the OPF provided different values of generation cost, emission, transmission loss, and maximum loadability. The results suggest that FVSI and Lmn provide the minimum generation cost values for all test systems, while VCPI and LVSI provide lower emission values for most systems. VCPI gives the lowest transmission losses for all systems, and the L‐index provides the maximum loadability for most systems. Incorporation of these indices into the OPF can provide the optimal values in different terms, and the most suitable voltage stability index will depend on the situation and size of the system, which needs to be judiciously chosen. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In this paper, a new particle swarm optimization-based approach is proposed for the geometrical optimization of the nanowires solar cells to achieve improved optical performance. The proposed hybrid approach combines the 3-D numerical analysis using accurate solutions of Maxwell's equations and metaheuristic investigation to boost the solar cell total absorbance efficiency. Our purpose resides on modulating the electric field and increasing the light trapping capability by optimizing the radial solar cell geometrical parameters. Moreover, a comprehensive study of vertical core-shell nanowire arrays optical parameters such as the integral absorption, reflection, and total absorbance efficiency is carried out, in order to reveal the optimized radial solar cells optical performance for low-cost photovoltaic applications. We find that the proposed hybrid approach plays a crucial role in improving the nanowires solar cells optical performance, where the optimized design exhibits superior total absorbance efficiency and lower total reflection in comparison with those provided by the conventional planar design. The obtained results make the proposed global optimization approach valuable for providing high-efficiency nanowires solar cells. [ABSTRACT FROM AUTHOR]

Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper presents a new form of Particle Swarm Optimization (PSO) based on the concept of tabu search (TS). In PSO, when a particle finds a local optimal solution, all of the particles gather around that one, and cannot escape from it. On the other hand, TS can escape from the local optimal solution by moving away from the best present solution. The proposed Tabu List PSO (TL-PSO) is a method for combining the strong points of PSO and TS. This method stores the history of pbest in a tabu list. When a particle has a reduced searching ability, it selects a pbest of the past from the historical values, which is used for the update. This makes each particle active, and the searching ability of the swarm makes progress. The proposed method was validated by numerical simulations with several functions that are well known as optimization benchmark problems for comparison to the conventional PSO method. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 172(4): 31–37, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20966 [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has lately become very popular in the electromagnetics domain. Although in some instances PSO shows a superior performance compared with other global optimization techniques, it is still computationally more expensive relative to classical gradient techniques. In this paper, a cooperative particle swarm optimization (CPSO) is adopted to achieve a faster convergence compared with the conventional PSO, while maintaining its main feature, which is the capability of finding global optimum. In order to deploy PSO more efficiently, the often neglected effect of the initial population on the overall convergence of PSO is discussed. It is shown that subdividing the space into grid cells and using random distribution within these cells will give the best results in terms of convergence speed. Different boundary conditions are tried on the CPSO algorithm. In order to verify the performance of the proposed algorithm, the algorithm is compared with the conventional PSO using six different objective functions. As a design example, an ultra-wide-band filter is designed. The results show a slightly faster convergence compared with the conventional PSO. The designed filter is fabricated and experimental results are also shown. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY: It has become desirable to develop energy control technologies for environmental issues such as global warming and exhaustion of fossil fuel. Power fluctuations in large power consumer facilities may cause instability of electric power systems and increase the cost of the electric power facilities and electricity charges. Developing electric power leveling systems (EPLS) to compensate power fluctuations is necessary for future electric power systems. EPLS with an SMES has been proposed as one countermeasure for use in electric power quality improvement. SMES is superior to other energy storage devices in response and storage efficiency. The authors have proposed EPLS based on fuzzy control with SMES. For this practical implementation, optimizing the control gain and SMES capacity is an important issue. This paper proposes a new method for optimization of the EPLS. The proposed algorithm is a novel particle swarm optimization based on taper‐off reflectance (TRPSO). The proposed TRPSO optimizes the design variables of the EPLS efficiently and effectively. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(3): 10–18, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com). DOI 10.1002/eej.22472 [ABSTRACT FROM AUTHOR]