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Voltage instability is a major challenge facing power system (PS) that has affected some organizations in achieving their desired goals. Therefore, voltage instability is the incapability of the PS to maintain the voltage standard under no disturbance and after subjecting to disruption. This paper describes the voltage instability phenomena; voltage stability indices include Line Stability Index (LP), Line voltage stability index, Fast Voltage Stability Index (FVSI), line stability factor (LQP), Bus voltage collapse prediction index (BVCPI), L index, voltage stability index (VCP-1), and so on. This review focuses on some stability indices that could identify the weak bus in the electrical PS network. The application of particle swarm optimization (PSO) to minimize losses that cause voltage instability is discussed. It started a detailed understanding of the power blackouts and the detrimental effects on the global economy. This was followed by a thorough understanding of the voltage instability/stability phenomenon, classification in power systems, and the corresponding formulations. The study presents an overview of voltage assessment techniques prior to applying PSO in discrete and multi-objective optimization and the corresponding advantages over others. These are followed by the progress and advances in voltage stability using PSO involving single and hybrid optimization methods. Lastly, to bridge the research gaps, the present study highlighted challenges and future prospects to foster further advancement in the field. [ABSTRACT FROM AUTHOR]

This paper proposes a new block encryption algorithm for cryptographic information protection. It describes a new transformation method EM (Exponentiation Module), which is part of the algorithm, and a method of S-box obtaining. The paper also considers an optimization technique to advance the efficiency of key selection and calculation. We discuss the possibility to obtain good results by applying the peculiar properties of cryptographic primitives in the Galois field. To increase the strength and speed of the encryption algorithm, we used a nonpositional polynomial notation and an indexed view for the Galois field. The paper provides for statistical properties of the ciphertext obtained with the developed algorithm. We also present the results of differential and linear cryptanalysis of the S-box used. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive robust proportional-integral-derivative (PID) controller for under-actuated dynamical systems via employing the advantages of the PID control and sliding surfaces. The related control gains as adjustable parameters are computed during the control process using gradient descent techniques and the chain derivative rule. Based on the design of control rules, the genetic algorithm optimization is utilized in order to find the optimal values of the learning rates and initial conditions of the control gains. The suggested strategy is implemented successfully to stabilize the cart-pole and ball-beam systems. Lastly, the simulation results demonstrate the efficacy of the proposed controller to control such under-actuated dynamical systems as well as its superiority in comparison with other recently introduced methods. [ABSTRACT FROM AUTHOR]

This paper addresses the tracking control problem of a single-arm manipulator actuated by Pneumatic Artificial Muscle (PAM) subjected to perturbation in parameters. The super-twisting sliding-mode control (STSMC) has been designed for the PAM system and the stability analysis has been presented to prove the asymptotic convergence of tracking errors. A perturbation observer-based on sliding-mode methodology has been proposed to estimate the uncertainty in system parameters and it is combined with STSMC to establish a robust controller against variation in parameters and to improve the capability of SMC in terms of chattering reduction. The stability analysis based on Lyapunov showed that the estimation error could converge asymptotically and the closed-loop system of an observer-based controlled PAM manipulator is asymptotically stable. Also, to improve the performance of the observer-based controlled system, a modern optimization technique based on Particle Swarming Optimization (PSO) has been suggested and developed for tuning the design parameters of the super-twisting sliding mode-controlled PAM robot based on Sliding-Mode Perturbation Observer (SMPO). The computer simulation based on MATLAB programming format showed the effectiveness of the both proposed observer and controller. [ABSTRACT FROM AUTHOR]

The problem of the optimal design of the life support systems of manned spacecraft is studied in this paper. The aim is to select components of all relevant subsystems to minimize the mass and power consumption and maximize the total technology readiness level while simultaneously satisfying the mission requirements and physical constraints. The model is developed by considering all intercommunication between the subsystems as the optimal assignment. The final problem is a mixed-integer, non-linear programming problem which is solved by a modified evolutionary optimization algorithm. A set of simulations is performed using real data, which demonstrates the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution system optimization. Furthermore, this article pointed out the environmental, economic, technological and regulatory drivers that lead to a rapid interest in the DG system based on renewable sources. A summary of popular meta-heuristic optimization tools discussed in table form with merits and demerits to increase fresh prospective paths to multi-approach that have not yet been studied. [ABSTRACT FROM AUTHOR]

The use of meta-heuristic methods for solving nonlinear engineering and optimization problems is one of the paramount topics that attracted the attention of the researchers. Particle swarm optimization (PSO) is an optimization algorithm which has inspired by birds flocking. However, like other methods, PSO has some disadvantages such as problems in finding the best global minimum or trapping in the local minima in some special problems. In some works, the initial particles are randomly set using uniform or Gaussian distributions. These particles sometimes fail to cover the search space completely. The main goal of this paper is to improve the mechanism of the initial population production stage in the first step to cover the feasible space properly. So, with the help of the scrambled Halton sequence and producing quasi-random numbers, the initial population has been generated in a mathematical way without making a lot of change in the original PSO algorithm and its structure. These particles cover the search space more efficiently. This new hybrid algorithm is named the Halton-PSO in this research. The results show that Halton-PSO improves the ability and efficiency of PSO. The performance and ability of the proposed Halton-PSO algorithm have been examined by 11 benchmark functions and 7 different nonlinear engineering problems. Both of the optimization results of test functions and the real problems demonstrate that the Halton-PSO method is more successful than the original PSO and the PSO family algorithms and the other methods for distinguishing the global best minimum. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) specifically from solar energy is gaining much attention. Integration of solar photo-voltaic plant (SPP) on distribution grid increases flexibility of the system but lacking behind in terms of convention protection scheme. The impact of orientation of point of common coupling (PCC) of SPP on distribution grid changes the entire power flow computation and hence, coordination of protection devices. This paper presents the power flow computation on modified IEEE 4-bus system for different integrated positions of SPP. Further analysis is carried for the fault current level with different locations and number of SPP DG. It has been found that DG integration increases the magnitudes of fault current; hence, it will have impact on relay co-ordination. A MATLAB/Simulink model is proposed for relay mis-coordination and optimal co-ordination by implementing evolutionary optimization technique. The Proposed study on IEEE 4-bus system along with three units 100 kW capacity of SPP is used to derived optimal co-ordination solution which validates the obtained results on electrical transient's analysis programming platform (ETAP). [ABSTRACT FROM AUTHOR]

Mean-variance mapping optimization (MVMO) is a new population-based metaheuristic technique which is successfully applied for different power system optimization problems. The special feature of MVMO is the mapping function applied for the mutation based on the mean and variance of n-best population. Recently, the modified version of MVMO has been developed to become more powerful, named as swarmbased mean-variance mapping optimization (MVMOS). This paper proposes MVMOS as a new approach for solving the economic dispatch (ED) problem considering valve-point effects. To validate the performance of the proposed method, the MVMOS is tested on three systems including 3, 13, and 40 thermal generating units with valve-point effects and the obtained results from MVMOS are compared to those from other existing methods in the literature. Test results have indicated that the proposed MVMOS is more robust and produces better solution quality than many other methods. Therefore, the MVMOS is efficient for solving the ED with valve-point effects. [ABSTRACT FROM AUTHOR]

This paper presents the performance of teaching-learning-based optimization (TLBO) algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching-learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem. [ABSTRACT FROM AUTHOR]

The Bees Algorithm is a parameter optimisation method that mimics the foraging behaviour of honey bees. This paper presents an experimental study of the performance of the Bees Algorithm. Its strengths and weaknesses are analysed, and the most suitable parameterizations in relation to different optimisation tasks are revealed. The robustness of the optimisation results to reasonable modifications of the fitness landscape is studied for a large set of parameterizations. The Bees Algorithm is tested on 18 custom-made function minimisation benchmarks, and its performance compared to that of two state-of-the-art biologically inspired optimisation methods. Thanks to their two-dimensional nature, the proposed fitness landscapes are easy to visualise. Experimental evidence indicates also that they constitute a varied and challenging set of test cases, useful to reveal the specific abilities and biases of the search algorithms. In addition, the performance of the Bees Algorithm and the other two optimisation methods is tested on four real-world minimisation tasks from the literature. The results obtained on the benchmarks prove the effectiveness and robustness of the bees foraging metaphor, in particular on the most deceptive and high-dimensional fitness landscapes. They also confirm the ability of the Bees Algorithm to solve complex real-world optimisation tasks. [ABSTRACT FROM AUTHOR]

This paper deals with mixed model assembly line (MMAL) balancing problem of type-I. In MMALs several products are made on an assembly line while the similarity of these products is so high. As a result, it is possible to assemble several types of products simultaneously without any additional setup times. The problem has some particular features such as parallel workstations and precedence constraints in dynamic periods in which each period also effects on its next period. The research intends to reduce the number of workstations and maximize the workload smoothness between workstations. Dynamic periods are used to determine all variables in different periods to achieve efficient solutions. A non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are used to solve the problem. The proposed model is validated with GAMS software for small size problem and the performance of the foregoing algorithms is compared with each other based on some comparison metrics. The NSGA-II outperforms MOPSO with respect to some comparison metrics used in this paper, but in other metrics MOPSO is better than NSGA-II. Finally, conclusion and future research is provided. [ABSTRACT FROM PUBLISHER]

This paper deals with automatic generation control of multi area power system using a Fuzzy PID controller. The controller parameters are optimized by Grey Wolf Optimizer (GWO) algorithm. Initially, Hydro-Thermal-Gas two area power systems is considered and superiority of the proposed controller is verified by comparing the results with GWO optimized classical PID controller as well as recently published optimal controller, such as DE-PID and TLBO-PID controllers. The proposed methodology is also verified with a modified power system with a nuclear plant and HVDC link and reveals better performance when compared with sliding mode controller tuned by TLBO algorithm. The proposed controller is designed to stabilize the frequency deviations of nonlinear power system considering FACTS devices and SMES. The results reveal that IPFC seems to be a promising alternative for frequency and tie-line power stabilization. Also the proposed controller is robust and satisfactory towards random step and sinusoidal load patterns. [ABSTRACT FROM AUTHOR]

In service and production area, problems such as severity of competition and increasing customers’ expectations, put the producers’ under a commitment to eliminate product defects and make up for any kind of shortage and deviations in its performance. In order to reach the mentioned aim, failure mode and effects analysis (FMEA) technique can be typically utilized for prevention, identification, control and elimination of possible errors. In this paper, FMEA approach is adopted to design and implement a system of quality control in the apparel production lines, to identify and rank the possible difficulties, and then finally, to issue the right commands to the quality control stations. For this purpose, the data are gathered from a medium-sized apparel factory and implemented by FMEA and then, improvability of production efficiency is analyzed using simulation-based optimization technique. The results of the simulation show significant decrease in product defects, rework, and total production cost. [ABSTRACT FROM AUTHOR]

This paper develops an optimization model for pricing a monopolistic application software in the presence of piracy. The purpose is raising revenue produced by product’s sale with determining prices in a price skimming strategy and minimizing amount of piracy. The model is a multifunctional price skimming optimization with simplex method which accompanied by a deterministic method for calculating time intervals of each segment. A linear function is used to describe demand of each segment. In addition, a linear piracy function is proposed to make piracy a dynamic parameter. The model has the ability to apply penetration pricing and controlling market share. Rough estimates of Windows 7 sale’s parameters are used to apply in the model. Optimizing case of Windows 7 is resulted in 7.3 percent increase in revenue while value of net market share is virtually constant. Therefore, the developed model demonstrates its competence in optimizing revenue by determining prices with presence of piracy. Results of the research show that to tackle piracy, range of price skimming must be decreased in a way that highest price need to be intensely reduced while lowest one must be slightly reduced. The benefit of using this strategy, is incurring lowest revenue loss due to piracy. The Effects of an escalation in piracy on proposed optimization model include increase in number of sale, demand, selling portion, market share, and decrease in price, price difference between segments, and revenue. [ABSTRACT FROM AUTHOR]

The automatic generation of procedures for combinatorial optimization problems is emerging as a new alternative to address the hardest problems of this class. One of these problems still offering great computational difficulty is the traveling salesman problem. Its simple presentation masks the great difficulty that exists when solving it numerically. The results obtained so far for this problem are based on the hybridization of known heuristics. However, there is still a need for an experimental breakthrough in the study of all of the possible combinations of heuristics, which represents a huge search space. In this paper, we explore this space using evolutionary computing to automatically design new algorithms for the problem. We carried out a computational experiment to produce the algorithms that not only are competitive with some of the existing heuristics but that also contain several novel structures that directly influence performance. [ABSTRACT FROM PUBLISHER]

This paper proposes a new level set method for topological shape optimization of continuum structure using radial basis function (RBF) and discrete wavelet transform (DWT). The boundary of the structure is implicitly represented as the zero level set of a higher-dimensional level set function. The interpolation of the implicit surface using RBF is introduced to decouple the spatial and temporal dependence of the level set function. In doing so, the Hamilton-Jacobi partial differential equation (PDE) that defines the motion of the level set function is transformed into an explicit parametric form, without requiring the direct solution of the complicated PDE using the finite difference method. Therefore, many more efficient gradient-based optimization algorithms can be applied to solve the optimization problem, via updating the expansion coefficients of the interpolant and then evolving the level set function and the boundary. Furthermore, the DWT is employed to handle the full matrix arising from the globally supported RBF interpolation. Several high stiffness but lightweight designs with smooth and clear structural boundaries are optimized and presented. The numerical results show that the proposed method can remarkably increase the efficiency in the topology optimization design of both the 2D and 3D structures. [ABSTRACT FROM PUBLISHER]

Hole-making is a type of machining processes that are specifically used to cut a hole into a part. The objective of interest in hole-making operations is to reduce the summation of tool airtime and tool switching time in order to reduce total processing time. This objective is affected by the sequence through which operations are done. This problem is formulated as a zero–one nonlinear mathematical programming model. In this paper, a dynamic programming-based method is developed to solve the proposed mathematical model and obtain the globally optimum solutions. An illustrative example is given to show the application and efficiency of the proposed method for optimizing the sequence of hole-making operations in a typical industrial part. The quality of solutions obtained by the proposed method is compared to those obtained by both branch-and-bound method and an ant algorithm available in the literature. The computational experiments reflect the high efficiency of our proposed method. [ABSTRACT FROM PUBLISHER]