Transient stability constrained optimal power flow using particle swarm optimisation

A novel approach based on the particle swarm optimisation (PSO) technique is proposed for the transient-stability constrained optimal power flow (TSCOPF) problem. Optimal power flow (OPF) with transient-stability constraints considered is formulated as an extended OPF with additional rotor angle inequality constraints. For this nonlinear optimisation problem, the objective function is defined as minimising the total fuel cost of the system. The proposed PSO-based approach is demonstrated and compared with conventional OPF as well as a genetic algorithm based counterpart on the IEEE 30-bus system. Furthermore, the effectiveness of the PSO-based TSCOPF in handling multiple contingencies is illustrated using the New England 39-bus system. Test results show that the proposed approach is capable of obtaining higher quality solutions efficiently in the TSCOPF problem     

A discrete particle swarm optimisation for operation sequencing in CAPP

Operation sequencing is one of crucial tasks for process planning in a CAPP system. In this study, a novel discrete particle swarm optimisation (DPSO) named feasible sequence oriented DPSO (FSDPSO) is proposed to solve the operation sequencing problems in CAPP. To identify the process plan with lowest machining cost efficiently, the FSDPSO only searches the feasible operation sequences (FOSs) satisfying precedence constraints. In the FSDPSO, a particle represents a FOS as a permutation directly and the crossover-based updating mechanism is developed to evolve the particles in discrete feasible solution space. Furthermore, the fragment mutation for altering FOS and the uniform and greedy mutations for changing machine, cutting tool and tool access direction for each operation, along with the adaptive mutation probability, are adopted to improve exploration ability. Case studies are used to verify the performance of the FSDPSO. For case studies, the Taguchi method is used to determine the key parameters of the FSDPSO. A comparison has been made between the result of the proposed FSDPSO and those of three existing PSOs, an existing genetic algorithm and two ant colony algorithms. The comparative results show higher performance of the FSDPSO with respect to solution quality for operation sequencing.     

Machining fixture layout optimisation under dynamic conditions based on evolutionary techniques

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.     

Optimisation for multi-part flow-line configuration of reconfigurable manufacturing system using GA

To facilitate the configuration selection of reconfigurable manufacturing systems (RMS) at the beginning of every demand period, it needs to generate K (predefined number) best configurations as candidates. This paper presents a GA-based approach for optimising multi-part flow-line (MPFL) configurations of RMS for a part family. The parameters of the MPFL configuration comprise the number of workstations, the number of paralleling machines and machine type as well as assigned operation setups (OSs) for each workstation. Input requirements include an operation precedence graph for each part, relationships between operations and OSs as well as machine options for each OS. The objective is to minimise the capital cost of MPFL configurations. A 0-1 nonlinear programming model is developed to handle sharing machine utilisation over consecutive OSs for each part which is ignored in the existing approach. Then a novel GA-based approach is proposed to identify K economical solutions within a refined solution space comprising the optimal configurations associated with all feasible OS assignments. A case study shows that the best solution found by GA is better than the optimum obtained by the existing approach. The solution comparisons between the proposed GA and a particle swarm optimisation algorithm further illustrate the effectiveness and efficiency of the proposed GA approach.     

Self-adaptiveness in particle swarm optimisation to enhance available transfer capability using thyristor-controlled series compensation (TCSC)

Available transfer capability (ATC) is one of the challenging criteria under the functioning of the deregulated power system. The high demand for improving ATC is generally met using flexible alternating current transmission system (FACTS) devices in the power system. However, it suffers from serious crisis during determination of the optimal location and compensation stage of FACTS. The present study uses thyristor-controlled series compensation (TCSC) devices in order to compensate for the limitation of FACTS. Further, a novel self-adapted particle swarm optimisation (SAPSO) algorithm is proposed in this study for enhancing ATC. Experiments are carried on three benchmark bus systems such as IEEE 24, IEEE 30 and IEEE 57. Performance and statistical analyses are carried out by comparing the proposed SAPSO with the conventional PSO. Eventually, the study proves the effectiveness of the proposed method in case of ATC enhancement.     

Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization

Task Scheduling is a complex combinatorial optimization problem and known to be an NP hard. It is an important challenging issue in multiprocessor computing systems. Discrete Particle Swarm Optimization (DPSO) is a newly developed swarm intelligence technique for solving discrete optimization problems efficiently. In DPSO, each particle should limit its communication with the previous best solution and the best solutions of its neighbors. This learning restriction may reduce the diversity of the algorithm and also the possibility of occurring premature convergence problem. In order to address these issues, the proposed work presents a hybrid version of DPSO which is a combination of DPSO and Cyber Swarm Algorithm (CSA). The efficiency of the proposed algorithm is evaluated based on a set of benchmark instances and the performance criteria such as makespan, mean flow time and reliability cost.     

Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator

A novel method using particle swarm optimisation (PSO) is proposed for optimising parameters of controllers of a wind turbine (WT) with doubly fed induction generator (DFIG). The PSO algorithm is employed in the proposed parameter tuning method to search for the optimal parameters of controllers and achieve the optimal coordinated control of multiple controllers of WT system. The implementation of the algorithm for optimising the controllers' parameters is described in detail. In the analysis, the generic dynamic model of WT with DFIG and its associated controllers is presented, and the small signal stability model is derived; based on this, an eigenvalue-based objective function is utilised in the PSO-based optimisation algorithm to optimise the controllers' parameters. With the optimised controller parameters, the system stability is improved under both small and large disturbances. Furthermore, the fault ride-through capability of the WT with DFIG can be improved using the optimised controller. Simulations are performed to illustrate the control performance.     

Non-smooth/non-convex economic dispatch by a novel hybrid differential evolution algorithm

This paper presents a novel stochastic optimisation approach to determining the feasible optimal solution of the economic dispatch (ED) problem considering various generator constraints. Many practical constraints of generators, such as ramp rate limits, prohibited operating zones and the valve point effect, are considered. These constraints make the ED problem a non-smooth/non-convex minimisation problem with constraints. The proposed optimisation algorithm is called self-tuning hybrid differential evolution (self-tuning HDE). The self-tuning HDE utilises the concept of the 1/5 success rule of evolution strategies (ESs) in the original HDE to accelerate the search for the global optimum. Three test power systems, including 3-, 13-and 40-unit power systems, are applied to compare the performance of the proposed algorithm with genetic algorithms, the differential evolution algorithm and the HDE algorithm. Numerical results indicate that the entire performance of the proposed self-tuning HDE algorithm outperforms the other three algorithms.     

Gene expression feature selection for prostate cancer diagnosis using a two‐phase heuristic–deterministic search strategy

Here, a two‐phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi‐objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache‐based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.Inspec keywords: cancer, biological organs, optimisation, feature extraction, search problems, particle swarm optimisation, pattern classification, geneticsOther keywords: biomarkers, gene expression feature selection, prostate cancer diagnosis, heuristic–deterministic search strategy, two‐phase search strategy, gene expression data, statistical filtering method, noisiest data, multiobjective optimisation, particle swarm optimisation algorithm, chaotic method, selection algorithm, discriminant genes, available challenging prostate cancer data, informative genes     

Design and optimisation of assembly technique for auto-body components

The dimensional quality of auto-body relates to the whole external appearance, wind noise, the effect of closing the door and even driving smoothness of vehicles. The assembly dimensional quality can be improved by optimising assembly operations between parts and reasonable allocating tolerances of components. A multi-attribute directed liaison graph is proposed to describe precedence relationships and key control characteristics (KCCs) to eliminate unfeasible assembly sequences. A hybrid particle swarm optimisation and genetic algorithm is presented to optimise assembly operations for selecting the best assembly sequence. Based on the above, tolerances of the optimal KCCs are designed by using NSGA-II algorithm according to assembly tolerances and manufacturing costs. To improve optimisation effectiveness, the initial population uses the orthogonal experimental design and sensitivity coefficients of KCCs to generate chromosomes. Finally, the work applied the method by illustrating the process of assembly sequence optimisation and tolerance allocation, and the results show that this case verified the proposed method.     

Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral?derivative controller in automatic voltage regulator system

Optimal tuning of proportional?integral?derivative (PID) controller parameters is necessary for the satisfactory operation of automatic voltage regulator (AVR) system. This study presents a combined genetic algorithm (GA) and fuzzy logic approach to determine the optimal PID controller parameters in AVR system. The problem of obtaining the optimal PID controller parameters is formulated as an optimisation problem and a real-coded genetic algorithm (RGA) is applied to solve the optimisation problem. In the proposed RGA, the optimisation variables are represented as floating point numbers in the genetic population. Further, for effective genetic operation, the crossover and mutation operators which can deal directly with the floating point numbers are used. The proposed approach has resulted in PID controller with good transient response. The optimal PID gains obtained by the proposed GA for various operating conditions are used to develop the rule base of the Sugeno fuzzy system. The developed fuzzy system can give the PID parameters on-line for different operating conditions. The suitability of the proposed approach for PID controller tuning has been demonstrated through computer simulations in an AVR system.     

Solving non-oriented two dimensional bin packing problem using evolutionary particle swarm optimisation

The non-oriented two-dimensional bin packing problem (NO-2DBPP) deals with a set of integer sized rectangular pieces that are to be packed into identical square bins. The specific problem is to allocate the pieces to a minimum number of bins allowing the pieces to be rotated by 90° but without overlap. In this paper, an evolutionary particle swarm optimisation algorithm (EPSO) is proposed for solving the NO-2DBPP. Computational performance experiments of EPSO, simulating annealing (SA), genetic algorithm (GA) and unified tabu search (UTS) using published benchmark data were studied. Based on the results for packing 3000 rectangles, EPSO outperformed SA and GA. In addition; EPSO results were consistent with the results of UTS indicating that it is a promising algorithm for solving the NO-2DBPP.     

Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach

The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.     

混合蝙蝠算法在包装废弃物逆向物流中的应用

目的为了提高蝙蝠算法(BA)求解包装废弃物逆向物流问题的性能。方法在标准BA算法的基础上提出混合蝙蝠算法(HBA)。首先,构建新型蝙蝠表达式,使BA算法适用于包装废弃物逆向物流问题的求解。其次,引入自适应惯性权重,改造蝙蝠速度更新公式;然后,引入粒子群算法(PSO),对每次迭代中任一随机蝙蝠进行粒子群操作;最后,利用HBA算法对企业实例和标准算例进行仿真测试。结果企业最优回收距离为776.63 km。与遗传算法(GA)、蚁群算法(ACO)和禁忌搜索算法(TS)相比,HBA算法能够求得已知最优解的标准算例个数最多为6个,求得最好解与已知最优解的平均误差最小为8.58%,平均运行时间最短为4.39s。结论 HBA算法的全局寻优能力、稳定性和运行速度均优于GA算法、ACO算法和TS算法。     

3D brain tumor segmentation in MRI images based on a modified PSO technique

Three-dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1-C modality.     

Optimal sub-band and strength selection for blind watermarking in wavelet domain

With increasing use of digital media, need for digital rights management has arisen. Watermarking is used to hide copyright protection information in the host medium. Hiding information to ensure digital right protection must ensure high imperceptibility and an acceptable level of robustness. In the watermark embedding, appropriate watermark strength and place selection in the host image is the most critical aspect of the whole process. Both watermark strength and place selection are considered as optimisation problems and are optimised using genetic algorithm (GA) and particle swarm optimisation (PSO). The watermark is embedded in the wavelet domain. With the proposed method optimal wavelet family, band, watermark strength and wavelet depth level are selected to ensure higher robustness and imperceptibility. The watermark is embedded in the selected bands of the wavelet packets. The band and wavelet depth is selected using GA and watermark strength is optimised using PSO method. The proposed method shows promising results against attacks on a variety of filters, i.e. low pass, high pass and median filters. Robustness results on JPEG compression and gaussian noise are also improved compared with the current approaches in practice.     

Radial basis probabilistic neural network for differential protection of power transformer

Protection of medium- and large-power transformers has always remained an area of interest of relaying engineers. Conventionally, the protection is done making use of magnitude of various frequency components in differential current. A novel technique to distinguish between magnetising inrush and internal fault condition of a power transformer based on the difference in the current wave shape is developed. The proposed differential algorithm makes use of radial basis probabilistic neural network (RBPNN) instead of the conventional harmonic restraint- based differential relaying technique. A comparison of performance between RBPNN and heteroscedastic-type probabilistic neural network (PNN) is made. The optimal smoothing factor of heteroscedastic-type PNN is obtained by particle swarm optimisation technique. The results demonstrate the capability of RBPNN in terms of accuracy with respect to classification of differential current of the power transformer. For the verification of the developed algorithm, relaying signals for various operating conditions of the transformer, including internal faults and external faults, were obtained through PSCAD/EMTDC. The proposed algorithm has been implemented in MATLAB.     

Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem

In this article, the genetic algorithm (GA) and fully informed particle swarm (FIPS) are hybridized for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. In the proposed hybrid genetic algorithm–fully informed particle swarm algorithm (HGFA), FIPS is a popular variant of the particle swarm optimization algorithm. A random key and the related mode list representation schemes are used as encoding schemes, and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. Furthermore, the existing mode improvement procedure in the literature is modified. The results show that the proposed mode improvement procedure remarkably improves the project makespan. Comparing the results of the proposed HGFA with other approaches using the well-known PSPLIB benchmark sets validates the effectiveness of the proposed algorithm to solve the MRCPSP.     

Unequal-area stochastic facility layout problems: solutions using improved covariance matrix adaptation evolution strategy,particle swarm optimisation,and genetic algorithm

Determining the locations of departments or machines in a shop floor is classified as a facility layout problem. This article studies unequal-area stochastic facility layout problems where the shapes of departments are fixed during the iteration of an algorithm and the product demands are stochastic with a known variance and expected value. These problems are non-deterministic polynomial-time hard and very complex, thus meta-heuristic algorithms and evolution strategies are needed to solve them. In this paper, an improved covariance matrix adaptation evolution strategy (CMA ES) was developed and its results were compared with those of two improved meta-heuristic algorithms (i.e. improved particle swarm optimisation [PSO] and genetic algorithm [GA]). In the three proposed algorithms, the swapping method and two local search techniques which altered the positions of departments were used to avoid local optima and to improve the quality of solutions for the problems. A real case and two problem instances were introduced to test the proposed algorithms. The results showed that the proposed CMA ES has found better layouts in contrast to the proposed PSO and GA.     

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.