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     

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.     

Dynamic available transfer capability computation using a hybrid approach

In a restructured electricity market, accurate evaluation of available transfer capability (ATC) with dynamic constraints is a challenging task. An approach to determine dynamic ATC, utilising the benefits of a direct method as well as time-domain simulation method, has been developed. Structure-preserving energy function model, which retains the topology of the network, along with transient stability limit, has been used to compute dynamic ATC for bilateral as well as multilateral transactions in an electricity market. Constant impedance as well as composite models for real power loads have been considered. A new contingency severity index, which takes into account the impact of transactions on the severity of the contingencies, has been proposed to reduce the list of credible contingencies to be considered in determining the ATC. To demonstrate the effectiveness of the proposed method, it has been tested on 10-machine, 39-bus New England system and a practical 60-machine, 246-bus Indian 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.     

Automatic clustering for generalised cell formation using a hybrid particle swarm optimisation

This paper considers the cell formation (CF) problem in which parts have alternative process routings and the number of machine cells is not known a priori. Very few studies address these two practical issues at the same time. This paper proposes an automatic clustering approach based on a hybrid particle swarm optimisation (PSO) algorithm that can automatically evolve the number and cluster centres of machine cells for a generalised CF problem. In the proposed approach, a solution representation, comprising an integer number and a set of real numbers, is adopted to encode the number of cells and machine cluster centres, respectively. Besides, a discrete PSO algorithm is utilised to search for the number of machine cells, and a continuous PSO algorithm is employed to perform machine clustering. Effectiveness of the proposed approach has been demonstrated for test problems selected from the literature and those generated in this study. The experimental results indicate that the proposed approach is capable of solving the generalised machine CF problem without predetermination of the number of cells.     

Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach

Traditionally, process planning and scheduling are two independent essential functions in a job shop manufacturing environment. In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed. Based on this model, a modern evolutionary algorithm, i.e. the particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem. To explore the search space comprehensively, and to avoid being trapped into local optima, the PSO algorithm has been enhanced with new operators to improve its performance and different criteria, such as makespan, total job tardiness and balanced level of machine utilisation, have been used to evaluate the job performance. To improve the flexibility and agility, a re-planning method has been developed to address the conditions of machine breakdown and new order arrival. Case studies have been used to a verify the performance and efficiency of the modified PSO algorithm under different criteria. A comparison has been made between the result of the modified PSO algorithm and those of the genetic algorithm (GA) and the simulated annealing (SA) algorithm respectively, and different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimising the IPPS problem.     

Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation

This paper describes a relational database system for semi-generative process planning for sheet metal parts that emulates expert system capabilities. The system integrates a feature-based relational database for the parts, a forward chaining rule-based strategy for machine selection, both global and feature-specific execution of the rules and a graph theoretic cost optimization model for optimal process plan selection. This system, which is currently being developed for a sheet metal fabrication company, suggests that, using the experience of shopfloor personnel, an efficient integration of feature-based process planning and expert system strategies can be accomplished.     

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.     

Design and simulation of equilateral triangular microstrip antenna using particle swarm optimization (PSO) and advanced particle swarm optimization (APSO)

In this paper a new design is proposed in microstrip antenna family. In this paper, a review design of microstrip antenna design using particle swarm optimization (PSO) and advanced particle swarm optimization (APSO) has been presented which optimizes the parameters and both results are compared. This technique helps antenna engineers to design, analyze, and simulate antenna efficiently and effectively. An advanced PSO driven antenna has been developed to calculate resonant frequency of slit-cut stacked equilateral triangular microstrip antenna. The paper presents simplicity, accuracy and comparison of result between PSO and APSO.     

Maximising manufacturing system efficiency for multi-characteristic linear assembly by using particle swarm optimisation in batch selective assembly

Quality of an assembly is mainly based on the quality of mating parts. Due to random variation in sources such as materials, machines, operators and measurements, even those mating parts manufactured by the same process vary in their dimensions. When mating parts are assembled linearly, the resulting variation will be the sum of the mating part tolerances. Many assemblies are not able to meet the assembly specification in the available assembly methods. This will decrease the manufacturing system efficiency. Batch selective assembly is helpful to keep the assembly requirement and also to increase the manufacturing system efficiency. In traditional selective assembly, the mating part population is partitioned to form selective groups, and the parts of corresponding selective groups are assembled interchangeably. After the invention of advanced dimension measuring devices and the computer, today batch selective assembly plays a vital role in the manufacturing system. In batch selective assembly, all dimensions of a batch of mating parts are measured and stored in a computer. Instead of forming selective groups, each and every part is assigned to its best matching part. In this work, a particle swarm optimisation based algorithm is proposed by applying the batch selective assembly methodology to a multi-characteristic assembly environment, to maximise the assembly efficiency and thereby maximising the manufacturing system efficiency. The proposed algorithm is tested with a set of experimental problem data sets and is found to outperform the traditional selective assembly and sequential assembly methods, in producing solutions with higher manufacturing system efficiency.     

A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines

The Assembly Line Part Feeding Problem (ALPFP) is a complex combinatorial optimisation problem concerned with the delivery of the required parts to the assembly workstations in the right quantities at the right time. Solving the ALPFP includes simultaneously solving two sub-problems, namely tour scheduling and tow-train loading. In this article, we first define the problem and formulate it as a multi-objective mixed-integer linear programming model. Then, we carry out a complexity analysis, proving the ALPFP to be NP-complete. A modified particle swarm optimisation (MPSO) algorithm incorporating mutation as part of the position updating scheme is subsequently proposed. The MPSO is capable of finding very good solutions with small time requirements. Computational results are reported, demonstrating the efficiency and effectiveness of the proposed MPSO.     

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.     

Identifying brain regions contributing to Alzheimer's disease using self regulating particle swarm optimization

In this article, we developed an approach for detecting brain regions that contribute to Alzheimer's disease (AD) using support vector machine (SVM) classifiers and the recently developed self regulating particle swarm optimization (SRPSO) algorithm. SRPSO employs strategies inspired by the principles of learning in humans to achieve faster and better optimization results. The classifiers for distinguishing subjects into AD patients and cognitively normal (CN) individuals were built using grey matter (GM) and white matter (WM) volumetric features extracted from structural magnetic resonance (MR) images. It could be observed from results that the classifier built using both GM and WM features provided accuracy of 89.26% which is better than the performance of classifiers built using either GM or WM features only. Moreover, consideration of clinical features in addition to volumetric features improves the accuracy further to 94.63% which is better than the performance reported by recent works in literature. In order to identify the brain regions that are important for AD vs CN classification problem, we used SRPSO to extract GM and WM features that yield better classification performance. Using 50 features identified by SRPSO, an accuracy of 89.39% was obtained which is close to the accuracy based on all features. The features identified by SRPSO were mapped back to the brain to identify brain regions that exhibit degeneration in AD. In addition to identifying areas known to be involved in AD like cerebellum, hippocampus, this helped in finding newer areas that might contribute towards AD.     

Inverse analysis using particle swarm optimization for detecting corrosion profile of rebar in concrete structure

In this paper, inverse analysis with the use of Particle Swarm Optimization (PSO) is developed for detecting the corrosion of reinforcing steel in concrete from a relatively small number of potential data measured on the concrete surface. PSO is a promising optimization method due to its simplicity of programming and comparable accuracy. In this proposed inverse analysis using PSO, corrosion profiles represent the location and size of reinforcing steel corrosion. In this method, candidate solution is modeled as a swarm of particles. The objective function, which is proportional to the cost function, is evaluated for the swarm of particles. This function is the difference between the calculated and measured potentials on the concrete surface. The calculated potentials on the surface of the concrete are obtained by solving the Laplace's equation by using the Boundary Element Method (BEM). The corroded and non-corroded parts of the reinforcing steel are represented by each polarization curve. Inverse analysis is carried out by minimizing the cost function using PSO. Examples of the numerical simulation were used to demonstrate the effectiveness of the proposed method. It shows that proposed inverse analysis had promising capability in detecting the corrosion profile of reinforcing steel in concrete.     

Pd embedded in porous carbon （Pd@CMK-3） as an active catalyst for Suzuki reactions： Accelerating mass transfer to enhance the reaction rate

Heterogeneous catalysts are promising candidates for use in organic reactions due to their advantages in separation, recovery, and environment compatibility. In this work, an active porous catalyst denoted as Pd embedded in porous carbon （Pd@CMK-3） has been prepared by a strategy involving immersion, ammonia- hydrolysis, and heating procedures. Detailed characterization of the catalyst revealed that Pd（0） and Pd（I1） species co-exist and were embedded in the matrix of the porous carbon （CMK-3）. The as-prepared catalyst has shown high activity toward Suzuki reactions. Importantly, if the reaction mixture was homogenized by two minutes of ultrasonication rather than magnetic stirring before heating, the resistance to mass transfer in the pore channels was significantly reduced. As a result, the reactions proceeded more rapidly and a four-fold increase in the turnover frequency （TOF） could be obtained. When the ultrasonication was employed throughout the entire reaction process, the conversion could also exceed 90% even without the protection of inert gas, and although the reaction temperature was lowered to 30 ℃. This work provides a method for fabricating highly active porous carbon encapsulated Pd catalysts for Suzuki reactions and proves that the problem of mass transfer in porous catalysts can be conveniently resolved by ultrasonication without any chemical modification being necessary.     

Tandem dye acceptor used to enhance upconversion fluorescence resonance energy transfer in homogeneous assays

In fluorescence resonance energy transfer (FRET)-based assays, spectral separation of acceptor emission from donor emission is a common problem affecting the assay sensitivity. The challenge derives from small Stokes shifts characteristic to conventional fluorescent dyes resulting in leakage of donor emission to the measurement window intended only to collect the acceptor emission. We have studied a FRET-based homogeneous bioaffinity assay utilizing a tandem dye acceptor with a large pseudo-Stokes shift (139 nm). The tandem dye was constructed using B-phycoerythrin as an absorber and multiple Alexa Fluor 680 dyes as emitters. As a donor, we employed upconverting phosphor particles, which uniquely emit at visible wavelengths under low-energy infrared excitation enabling the fluorescence measurements free from autofluorescence even without time-resolved detection. With the tandem dye, it was possible to achieve four times higher signal from a single binding event compared to the conventional Alexa Fluor 680 dye alone. Tandem dyes are widely used in cytometry and other multiplex purposes, but their applications can be expanded to fluorescence-based homogeneous assays. Both the optimal excitation and emission wavelengths of tandem dye can be tuned to a desired region by choosing appropriate fluorophores enabling specifically designed acceptor dyes with large Stokes shift.