A GA-based initialization of PSO for optimal APFS allocation in water desalination plant

In this paper we investigate the application of the Particle Swarm Optimization (PSO) technique for solving the Hardware/Software partitioning problem. The PSO is attractive for the Hardware/Software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. We carried out several tests on a hypothetical, relatively-large Hardware/Software partitioning problem using the PSO algorithm as well as the Genetic Algorithm (GA), which is another evolutionary technique. We found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, we tested several hybrid combinations of PSO and GA algorithms; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. We propose to name this successive PSO algorithm as the Re-excited PSO algorithm. The constrained formulations of the problem are investigated for different tuning or limiting design parameters constraints.

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.

In this paper, a novel particle swarm optimization (PSO) technique is proposed to tune the proportional-integral (PI) controller gain parameters for enhancing the dynamic performance of the shunt active power filter (APF). The shunt APFs are well established filter to compensate current harmonics, reactive power to maintain the power factor unity. The compensation is highly influenced by the DC-link voltage regulation. The calculated PI controller gain parameters conventionally, are giving satisfactory results under steady state condition of the load. However, tuning of the PI controller parameters under fast changing loads are very difficult. To improve the dynamic performance of the system and optimize the gain parameters of the PI controller, a PSO technique is proposed. The modified p-q theory uses a composite observer filter to extract fundamental component of voltage from the distorted supply voltage for the further process of calculating reference current. A complete comparison of conventional and PSO based PI controller gain tuning have been simulated using MATLAB® Simulink software under different supply voltage and load condition of the system. The results show that the dynamic response is improved with PSO based PI tuning compared to conventional PI tuning.

Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design

System identification in noisy environment has been a matter of concern for researchers in many disciplines of science and engineering. In the past the least mean square algorithm (LMS), genetic algorithm (GA) etc. have been employed for developing a parallel model. During training by LMS algorithm the weights rattle around and does not converge to optimal solution. This gives rise to poor performance of the model. Although GA always ensures the convergence of the weights to the global optimum but it suffers from slower convergence rate. To alleviate the problem we propose a novel Particle Swarm Optimization (PSO) technique for identifying nonlinear systems. The PSO is also a population based derivative free optimization technique like GA, and hence ascertains the convergence of the model parameters to the global optimum, there by yielding the same performance as provided by GA but with a faster speed. Comprehensive computer simulations validate that the PSO based identification is a better candidate even under noisy condition both in terms of convergence speed as well as number of input samples used.

In this paper the authors investigate the application of the particle swarm optimization (PSO) technique for solving the hardware/software partitioning problem. The PSO is attractive for the hardware/software partitioning problem as it offers reasonable coverage of the design space together with O(n) main loop's execution time, where n is the number of proposed solutions that will evolve to provide the final solution. The authors carried out several tests on a hypothetical, relatively-large hardware/software partitioning problem using the PSO algorithm as well as the genetic algorithm (GA), which is another evolutionary technique. The authors found that PSO outperforms GA in the cost function and the execution time. For the case of unconstrained design problem, the authors tested several hybrid combinations of PSO and GA algorithm; including PSO then GA, GA then PSO, GA followed by GA, and finally PSO followed by PSO. We found that a PSO followed by GA algorithm gives small or no improvement at all, while a GA then PSO algorithm gives the same results as the PSO alone. The PSO algorithm followed by another PSO round gave the best result as it allows another round of domain exploration. The second PSO round assign new randomized velocities to the particles, while keeping best particle positions obtained in the first round. The paper proposes to name this successive PSO algorithm as the re-excited PSO algorithm

Performance evaluation of PSO and GA in PWR core loading pattern optimization

Adaptive filtering of EEG/ERP through noise cancellers using an improved PSO algorithm

Mathematical programming problems (MPPs) having unique mathematical format usually treated as hard problems are important optimization problems. On the other hand, particle swarm optimization (PSO) and genetic algorithm (GA) are important optimization approaches used in solving complex optimization problems. PSO and GA are nature-inspired techniques based on the social behavior of species and operations of human chromosomes, respectively. During past decades, PSO, GA, and their hybrid techniques were extensively used in solving different types of mathematical programming problems. But there is a lack of comprehensive review analysis on PSO, GA, and associated hybrid techniques for solving various types of mathematical programming problems. In this article, we present a systematic detailed review on these techniques in context of solving mathematical programming problems along with comprehensive review analysis. A systematic review procedure has been used for analysis of sixty-eight articles of reputed databases like Thomson Reuters, Web of Science, Scopus, and IEEE. The research gaps and future research scope for the researcher who inclines to solve different types of mathematical programming problems with these techniques are also identified. Relevance of the work: Mathematical programming problems (e.g., linear programming problem (LPP), NLPP, MOPP, etc.) are important optimization problems in which many types of real-world problems are formulated. On other hand, PSO and GA are prominent natured-inspired techniques used to solve complex optimization problems. This article presents comprehensive review analysis on PSO and GA in context of solving different types of mathematical programming problems. This review work also narrates the unique linkage between GA–PSO techniques and mathematical programming problems. This systematic and in-depth review analysis will be useful to the researchers and scientists working in the related areas of mathematical programming and natured-inspired optimization techniques to pursue carrying out further research in these areas.KeywordsParticle swarm optimization (PSO)Genetic algorithm (GA)Hybrid of PSO and GALinear and nonlinear programming problem

The renewable‐based hybrid energy storage systems have gained significant attention in recent times, due to their increased power extraction efficiency, cost‐effectiveness, and eco‐friendly nature. But, the power management, optimal sizing of components, economic cost of energy, and system reliability are considered as the major problems of hybrid energy storage systems. For this purpose, the different types of optimization methodologies are developed in the conventional works for optimizing the size and cost of hybrid energy systems. The main contribution of this work is to design an efficient and reliable hybrid energy storage system based on the combination of solar, wind, biomass, batteries, and generators with optimal sizing of components, and reduced system cost. Hence, two different types of meta‐heuristics optimization techniques such as genetic algorithm (GA), and particle swarm optimization (PSO) are validated and compared with select the most suitable one for the reliable hybrid energy systems. Here, the mathematical modeling of a hybrid energy sources are presented for generating the electricity. It also discussed about the operating principles, working nature, flow of modeling, advantages, and disadvantages of GA and PSO techniques. During simulation, the performance of these algorithms is evaluated and compared by using various measures. Due to the increased convergence rate, reduced overfitting, and local optimum the performance of PSO is highly improved, when compared with the GA. Also, the PSO is discovered to perform better than the GA because it concurrently executes global and local searches, whereas the GA focuses primarily on the global search. The total excess energy for the entire year is estimated to be 5139 kWh/yr based on the findings. The PSO algorithm accurately predicts solar PV, wind turbines, batteries, and biomass gasifier with an ASC of 63 006$/yr and an LCOE of 0.173$/kWh. Hence, the results of PSO are superior than the standard GA mechanism. Moreover, the obtained results indicate that the PSO algorithm provides the better results compared with GA and HOMER.

This paper introduces the application of particle swarm optimization (PSO) technique to identify the parameters of pole-zero plants or infinite impulse response (IIR) systems. The PSO is one of the evolutionary computing tools that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge to a suitable solution with low computational complexity. This paper applies this powerful PSO tool to identify the parameters of standard IIR systems and compares the results with those obtained using the genetic algorithm (GA). The comparative results reveal that the PSO shows faster convergence, involves low complexity, yields minimum MSE level and exhibits superior identification performance in comparison to its GA counterpart.

Flexible Alternating Current Transmission Systems, called FACTS, got in the recent years a well-known term for higher controllability in power systems by means of power electronic devices. FACTS-devices can effectively control the load flow distribution, improve the usage of existing system installations by increasing transmission capability, compensate reactive power, improve power quality, and improve stabilities of the power network. However, the location of these devices in the system plays a significant role to achieve such benefits. This paper presents the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques for finding out the optimal number, the optimal locations, and the optimal parameter settings of multiple Thyristor Controlled Series Compensator (TCSC) devices to achieve a maximum system loadability in the system with minimum installation cost of these device. The thermal limits of the lines and the voltage limits for the buses are taken as constraints during the optimization. Simulations are performed on IEEE 6-bus and IEEE 14-bus power systems. The obtained results are encouraging, and show that TCSC is one of the most effective series compensation devices that can significantly increase the system loadability. Also the results indicate that both GA and PSO techniques can easily and successfully find out the optimal variables, but PSO is faster than GA from the time perspective.

The solutions used to solve bi-level congestion pricing problems are usually based on heuristic network optimization methods which may not be able to find the best solution for these type of problems. The application of meta-heuristic methods can be seen as viable alternative solutions but so far, it has not received enough attention by researchers in this field. Therefore, the objective of this research was to compare the performance of two meta-heuristic algorithms namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with each other and also with a conventional heuristic method in terms of degree of optimization, computation time and the level of imposed tolls. Hence, a bi-level congestion pricing problem formulation, for simultaneous optimization of toll locations and toll levels on a road network, using these two meta-heuristic methods, was developed. In the upper level of this bi-level problem, the objective was to maximize the variation in the Net Social Surplus (NSS) and in the lower level, the Frank-Wolfe user equilibrium method was used to assign traffic flow to the road network. PSO and GA techniques were used separately to determine the optimal toll locations and levels for a Sioux Falls network. The numerical results obtained for this network showed that GA and PSO demonstrated an almost similar performance in terms of variation in the NSS. However, the PSO technique showed 45% shorter run time and 24% lower mean toll level and consequently, a better overall performance than GA technique. Nevertheless, the number and location of toll links determined by these two methods were identical. Both algorithms also demonstrated a much better overall performance in comparison with a conventional heuristic algorithm. The results indicates the capability and superiority of these methods as viable solutions for application in congestion pricing problems.

This paper presents a design of adaptive hysteresis current controller (AHCC) by optimal tuning of PI regulator using PSO technique for a distribution static compensator (D-STATCOM). It is desirable to have optimal values of K p and K i in a PI regulator to decreases steady state error and improve stability of the system. This generates accurate values of reference injected currents and compare with instantaneous injected currents with help of AHCC in D-STATCOM. Hence to mitigate the source current harmonics and to improve the power quality in distribution system under different load considerations. The dynamic model of D-STATCOM has been adopted with AHCC by using MATLAB/Simulink. The optimal value K p and K i in a PI regulator are computed using particle swarm optimization (PSO) technique and the results are verified by genetic algorithm (GA). The simulations result show that the proposed PSO technique gives minimum error and optimally chosen parameters of PI, as compared to GA technique.

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. In this paper both PSO and GA optimization are employed for finding stable reduced order models of single-input- single-output large-scale linear systems. Both the techniques guarantee stability of reduced order model if the original high order model is stable. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example from literature and the results are compared with recently published conventional model reduction technique.