Particle swarm optimization with genetic recombination: a hybrid evolutionary algorithm

Flexible Alternating Current Transmission Systems (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 and settings of these devices in the system plays a significant role to achieve such benefits. This work presents the application of Real Coded Genetic Algorithm (RGA) and Particle Swarm Optimisation (PSO) for finding out the optimal locations, and the optimal parameter settings of multi type FACTS devices to achieve maximum system loadability (MSL) in the power system. The FACTS devices used are Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC). The reactance model of TCSC and the decoupled model of UPFC are considered in this work. The thermal limits of the line and voltage limits of the buses are taken as constraints during the optimization. Simulated Binary Crossover (SBX) and Non-uniform polynomial mutation are employed to improve the performance of the Genetic Algorithm used. Simulations are performed on IEEE 6 bus and 30 bus power systems. The obtained results are encouraging and show the effectiveness of RGA over PSO algorithm

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

Optimum PID controller design using real coded genetic algorithm (RGA) and particle swarm optimization (PSO) is addressed in this paper. The lower and upper bound of the design variables (KP, TI and TD) are selected in an intelligent manner around the values obtained from Ziegler-Nichols method. PID controller was designed by minimizing various performance measures such as ISE, IAE and ITAE for two different linear systems. The performance of RGA and PSO is compared with respect to time-response specifications, computation time and statistical performance in 20 independent trials. The simulation reveals that the performance of PSO and RGA with simulated binary crossover (SBX) produced almost same time-domain performances and take same computation time. Also, it is found that PSO converges with less number of functional evaluations, consistent for simple systems and in general ITAE is preferable for quick settling time.

Implementing a diffusion model optimized by a hybrid evolutionary algorithm to forecast notebook shipments

The aim of this research is to apply the modified flower pollination algorithm (MoFPA) to the multiplevehicle routing problems (MVRP) with the time constraints. As one of the latest enhanced versions of the originalflower pollination algorithm (FPA), the MoFPA utilizes the randomly switching probability for selection betweenlocal (self) and global (cross) pollinations in order to search for the better solutions and improve its searchperformance. In this paper, the MoFPA is applied to solve the MVRP problems with the time constraints basedon the modern optimization approach. Ten standard MVRP problems with the time constraints consisting ofapproximately 100–500 destinations are selected and solved by the MoFPA. Results obtained by the MoFPA arecompared with those obtained by original FPA, genetic algorithm (GA), tabu search (TS) and particle swarmoptimization (PSO). Results reveals that the MoFPA can provide optimal solutions of all ten selected MVRPproblems with shorter total distance than FPA, PSO, TS and GA, respectively. In addition, the execution timeconsumed by FPA and MoFPA are less than PSO, TS and GA, respectively.

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.

Optimisation of fixture layout is critical to reduce geometric and form error of the workpiece during the machining process. In this paper the optimal placement of fixture elements (locator and clamp locations) under dynamic conditions is investigated using evolutionary techniques. The application of the newly developed particle swarm optimisation (PSO) algorithm and widely used genetic algorithm (GA) is presented to minimise elastic deformation of the workpiece considering its dynamic response. To improve the performances of GA and PSO, an improved GA (IGA) obtained by basic GA (GA) with sharing and adaptive mutation and an improved PSO (IPSO) obtained by basic PSO (PSO) incorporated into adaptive mutation are developed. ANSYS parametric design language (APDL) of finite element analysis is employed to compute the objective function for a given fixture layout. Three layout optimisation cases derived from the high speed slot milling case are used to test the effectiveness of the GA, IGA, PSO and IPSO based approaches. The comparisons of computational results show that IPSO seems superior to GA, IGA and PSO approaches with respect to the trade-off between global optimisation capability and convergence speed for the presented type problems.

Modeling and prediction for discharge lifetime of battery systems using hybrid evolutionary algorithms

Camera calibration is an essential issue in many computer vision tasks in which quantitative information of a scene is to be derived from its images. It is concerned with the determination of a set of parameters from the given images. In literature, it has been modeled as a nonlinear global optimization problem and has been solved using various optimization techniques. In this article, a recently developed variant of a very popular global optimization technique—the particle swarm optimization (PSO) algorithm—has been used for solving this problem for a stereo camera system modeled by pin-hole camera model. Extensive experiments have been performed on synthetic data to test the applicability of the technique to this problem. The simulation results, which have been compared with those obtained by a real coded genetic algorithm (RCGA) in literature, show that the proposed PSO performs a bit better than RCGA in terms of computational effort.

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO – evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.

Microarray technology in the last decade has been widely applied to detect correlated biomarkers in biological processes. Due to the need for analyzing massive amounts of generated data in this technology, computational intelligence approaches are used increasingly in this field. Biclustering algorithms are one of the most important of these techniques in microarray analysis. Two aspects of search mechanisms and biologists' desired patterns are the most essential issues in the design and evaluation of these algorithms. Different patterns can be achieved by considering different metrics. In this paper, Transposed Virtual Error (VET) is used as main metric. Also a hybrid evolutionary algorithm is proposed based on Evo-Bexpa algorithm which is introduced with VET. The proposed method is developed based on Genetic Algorithm (GA) used in Evo-Bexpa and Asexual Reproduction Optimization (ARO). The results indicate that underlying algorithm against Evo-Bexpa is more efficient in finding biclusters.

In this paper, a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) is used for the design of 8th order infinite impulse response (IIR) digital filter. The proposed optimization technique is a global heuristic search algorithm and better exploration and exploitation of multidimensional search space can be achieved with closely mimicked swarm behaviour in fundamental PSO equation. Performance of the proposed optimization technique is compared with some well accepted evolutionary algorithms such as PSO and real coded genetic algorithm (RGA). From the simulation study it is established that the CRPSO outperforms RGA and PSO, not only in the accuracy of the designed filter but also in the convergence speed and solution quality, i.e., the stop band attenuation, transition width, pass band and stop band ripples. Further, the pole-zero analysis justifies the stability of the designed optimized IIR filter.

The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.

Estimation of transformer parameters from nameplate data by imperialist competitive and gravitational search algorithms

The complexity of embedded system design increase as the technology keeps evolving from day to day. Hardware software partitioning has been a promising approach to solve this design problem of complexity in the embedded systems, by providing a solution that automatically decides the partitioning. A lot of research has been done to automate the partitioning which focusing on exact and heuristic algorithm. Then due to the slow performance of the exact algorithms, the study focus shift to heuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In this research the performance of both PSO algorithm and GA are analyzed in the application of the partitioning. In order to get the best among these two algorithms, hybrid combination across the two algorithms is designed. The best cost and their average time to achieve it are compared among PSO, GA and hybrid design. As a result, the graph obtained from the hybrid GA-GA-PSO required a smaller number of iterations to reach best cost. Compared to previous work, GA-GA-PSO obtained a smooth as the successive PSO graph. In conclusion, a new idea of hybrid across PSO and GA has been introduced and it results into a better solution for Hardware Software Partitioning.