改进蜂群算法在平面度误差评定中的应用

The satellite image segmentation is a key area for current research and numerous work has been done for exploration of this area. The nature inspired optimization algorithms are very promising with image segmentation techniques to provide a platform for processing of satellite images. In this paper a literature review of different nature based optimization algorithms such as modified artificial bee colony (MABC) algorithm, ABC algorithm, particle swarm optimization (PSO), Darwinian PSO, genetic algorithm (GA), Wind driven optimization (WDO) and cuckoo search(CS) using different objective functions has been discussed to find the optimized multilevel thresholds. These nature influenced optimization methods and their performances are compared using different objective functions for optimal multilevel thresholding. The comparative study shows that the MABC algorithm and different variants of CS algorithm are very strong and accurate in results generating using image segmentation. Both methods search multilevel thresholds very efficiently and correctly, and in MABC an improved bee’s search solution are used and in CS the cuckoo search solution are used.

ECG heart beat classification method based on modified ABC algorithm

This paper enhances the hierarchical fuzzy model to deal with the classification problems by adopting evolutionary genetic algorithm (GA) with a modified artificial bee colony (ABC) algorithm. Traditionally, fuzzy classifier could not provide a sufficiently high classification rate in higher feature dimension with few rules. In the literature, the genetic algorithm can take advantage from the global searching; moreover, the characteristic of ABC can enhance the local searching. Therefore, the hierarchical fuzzy model integrates GA with a modified ABC algorithm is constructed in this study to recognize some classification problems. The classification simulation includes three benchmark databases such as Glass, Wine, and Iris database. The result demonstrates that using evolutionary GA and modified ABC algorithm is beneficial than that without turning. Therefore, it is clearly that our methodology considers not only the global exploration but also the local exploitation.

Artificial bee colony (ABC) algorithm, explored in recent literature, is an efficient optimization technique which simulates the foraging behavior of honeybees. ABC algorithm is good at exploration but poor at exploitation. This paper presents a new modified ABC algorithm for numerical optimization problems to improve the exploitation capability of the ABC algorithm. A different probability function and a new searching mechanism are proposed. The modified ABC algorithm is tested on seven numerical optimization problems. The results demonstrate that the modified ABC algorithm outperforms the ABC algorithm on solution quality and faster convergence.

Based on the coupled vertical-torsional free vibration records of the spring-suspended section model of bridge decks, an improved Artificial Bee Colony (ABC) algorithm is proposed to identify the flutter derivatives. The ABC algorithm is a novel biological-inspired optimization algorithm, comparing with other iteration methods, the ABC algorithm can facilitate the identification process with no need for initial value. The standard ABC algorithm is good at exploration but poor at exploitation. To enhance the exploitation of the ABC algorithm, Powell’s method is used as a local search tool and a nonlinear factor is introduced for convergence control. Search equations applied to generate candidate solutions in the onlookers and scout bees phase are modified to improve the search ability of this method. The objective function in the optimization is weighted residual sum of squares between measured records and predicted values, where the weighting factors are optimized by an iteration process. The modified ABC algorithm is tested on several benchmark functions compared with the standard ABC algorithm. Simulation results show that the proposed algorithm has a higher convergence speed, better solution quality and stronger robustness. In order to investigate the effectiveness of the modified ABC algorithm in the flutter derivatives identification, numerical simulations of an ideal thin-plate model are carried out. The identification results show that the modified ABC algorithm for flutter derivatives identification of bridge decks is robust and reliable.

Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.

Improving flux vector control (FOC) drives performance is studied for five phase saturated model of induction motor. Fuzzy logic, genetic algorithm (GA) and particle swarm optimisation (PSO) are used for this purpose. Optimum flux reference identification by using FL, GA and PSO is used to minimise the motor input power to have the optimum motor efficiency. Selecting of the optimal gains using both methods is done to improve the motor response and behaviour. A comparison between the simulation results was done to evaluate the performance for the developed controller adopting (GA) and (PSO) algorithms. The results show that the proposed PSO controller algorithm has better optimisation performance than the proposed GA both for gain tuning and for the selection flux set point, while the fuzzy logic give the best performance for optimal flux set point selection.

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.

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.

Hybrid design of PID controller for four DoF lower limb exoskeleton

When a large number of tasks request different resources in a grid system, resources allocation should have been done with proper planning and scheduling to guarantee a good quality of service (QOS). There are different ways to provide these requests by choosing appropriate resources allocation to optimize the total operation of the system. In this study, first some parameters, such as priority, delay, reliability and cost are determined for each task to maximize system performance and appropriate resources distribution. Then, a hybrid optimization algorithm for choosing grid resource based on genetic algorithm and particle swarm optimization (PSO) is presented. Based on the experimental results, this method’s performance is 17.5% more than genetic and PSO algorithms in average. Key words: Grid system, resources selection, quality of service, genetic algorithm, particle swarm optimization (PSO).

This study aimed to optimize Adaptive Neuro-Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (α) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full-scale results of the driven piles. The input parameters used in this study were pile diameter (m), pile length (m), relative density (Id), embedment ratio (L/D), both of the pile end resistance (qc) and base resistance at relatively 10% base settlement (qb0.1) from CPT result, whereas the output was α. A learning fuzzy-based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real-monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R2, RMSE, and VAF. According to R2, RMSE, and VAF, values of (0.9439, 0.0123 and 99.91), (0.9872, 0.0117 and 99.99), and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA–ANFIS, and PSO–ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA–ANFIS model in estimating α ratio in driven shafts.

Multicast routing is an effective way to communicate among multiple hosts in a network. For multimedia applications, the routing algorithms should consider many Quality of Service (QoS) parameters such as delay, cost and so on to find a new route. However, to find routes with two or more QoS parameters is an NP-hard problem. This paper describes a new evolutionary scheme for optimization of multicast QoS routing based on the hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The upper-half of the best-performing individuals in a population are regarded as elites. Instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. This scheme can simultaneously optimize the cost of the tree, the maximum end-to-end delay, the average delay and the maximum link utilization. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. It has revealed an efficient method of the reconstruction of multicast tree topology and the experimental results demonstrated better performance than the conventional GA optimization method.

As an indispensable constituent of the premises of highly precious control of vertical takeoff and landing (VTOL) aircrafts, parameter identification has received an increasingly considerable attention from academic community and practitioners. In an effort to tackle the matter better, we herewith put forward a PID controlling particle swarm optimizer (PSO) which we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P idSO). It uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles' local and global best positions information. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO features better search effectiveness and efficiency in solving most of the multimodal optimization problems when compared with other recent variants of PSOs, and its performance can be upgraded by adopting proper control law based controllers. Moreover, PidSO, together with least squares (LS) method and genetic algorithm (GA), is applied to the parameter estimation of the VTOL aircraft. In comparison with LS method and GA, PidSO is a more effective tool in estimating the parameters of the VTOL aircraft.

ABSTRACTThis article introduces a method of mistuned parameter identification which consists of static frequency testing of blades, dichotomy and finite element analysis. A lumped parameter model of an engine bladed-disc system is then set up. A bladed arrangement optimization method, namely the genetic particle swarm optimization algorithm, is presented. It consists of a discrete particle swarm optimization and a genetic algorithm. From this, the local and global search ability is introduced. CUDA-based co-evolution particle swarm optimization, using a graphics processing unit, is presented and its performance is analysed. The results show that using optimization results can reduce the amplitude and localization of the forced vibration response of a bladed-disc system, while optimization based on the CUDA framework can improve the computing speed. This method could provide support for engineering applications in terms of effectiveness and efficiency.