Time-varying Acceleration Coefficients Research Articles

A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP)

A bi-objective Fuel efficient Green Vehicle Routing Problem (F-GVRP) with varying speed constraint is discussed in this paper as an extension of Green Vehicle Routing Problem (G-VRP). F-GVRP is modelled to minimize both route cost and fuel consumption using goal programming. The problem is solved using Particle Swarm Optimization with Greedy Mutation Operator and Time varying acceleration coefficient (TVa-PSOGMO). The objective of this paper is to study the behaviour of F-GVRP under varying speed environment and its impact on the route cost and fuel consumption. Experiments are conducted with constant and varying speed constraints and it is observed that better routing plan with minimum fuel consumption can be achieved under varying speed environment.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units

With increasing penetration of renewable energy sources, it is necessary to analyze its impact on the allocation of optimal power generation schedule. In this work, the pumped storage hydrothermal (PSHT) system incorporating solar units has been undertaken. The novel integrated heuristic approach named as time varying acceleration coefficient particle swarm optimization with mutation strategies (TVAC-PSO-MS) has been proposed. In this approach, an initial solution has been updated by the TVAC-PSO approach and then local best solutions are updated by using the successive mutation strategies namely Cauchy, Gaussian, and opposition based mutations. The Cauchy mutation strategy is applied to enhance the search capability and the Gaussian, as well as the opposition based mutation strategies are used to improve the exploitation capability of the algorithm. In order to validate the proposed approach, a standard test system of hydrothermal generation scheduling has been undertaken and the results have been compared with other state of art algorithms. Further, the proposed approach has been applied to optimize the cost of the PSHT system incorporating solar units and validate it through statistical test.

Whale optimization algorithm based optimal reactive power dispatch: A case study of the Algerian power system

Optimal reactive power dispatch (ORPD) is a particular case of the optimal power flow. ORPD was usually considered as the minimization of an objective function representing the total active power losses in the electrical networks. The constraints involved are the generator voltages, tap regulating transformers ratios and the amount of reactive shunt compensators. The goal of this study is to find the best vector of control variables, so that the power loss decreasing can be realized. In this paper, a new metaheuristic technique inspired from the bubble-net hunting technique of humpback whales, namely whale optimization algorithm (WOA), has been applied to solve the ORPD problem. The WOA method has been examined and confirmed on the IEEE 14-bus, IEEE 30-bus, in addition to a practical and large scale Algerian electric 114-bus test system. The obtained outcomes have been compared with two own developed methods, namely, particle swarm optimization (PSO) and particle swarm optimization with time varying acceleration coefficients (PSO-TVAC). Afterwards, an analysis of variance (One-way ANOVA test) has been implemented in order to verify the performance of our proposed algorithm in solving the ORPD problem. In summary, the comparison study shows the potential of this recent optimization method, and proves its robustness and effectiveness in solving the ORPD problem.

Research of converter transformer fault diagnosis based on improved PSO-BP algorithm

To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.

New self‐organising hierarchical PSO with jumping time‐varying acceleration coefficients

Without doubt, one of the powerful and effective optimiser in the area of evolutionary algorithms and improved particle swarm optimisation (PSO) is the self-organising hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC) which has been implemented successfully in the many problems (cited by 2430 until now). Real-world problems are multi-variable problems with real-world different complexities. The classical HPSO-TVAC optimisation technique often converges to local optima solution for some of the real-world problems. Therefore, finding efficient modern versions of the PSO algorithm (here HPSO-TVAC) to solve the real-world problems are absorbing a growing attention in recent years. A novel HPSO-TVAC algorithm for real-world optimisation is proposed. The simulation results show that proposed HPSO-TVAC new version, NHPSO-JTVAC, is powerful and very competitive for real-world optimisation.

Economic dispatch of multiple energy carriers

Energy hubs can provide high flexibility in the operation of the integrated systems. This paper presents Time Varying Acceleration Coefficients Particle Swarm Optimization (TVAC-PSO) algorithm to optimize the Multiple Energy Carriers Economic Dispatch (MECED) problem for hybrid electrical and natural gas networks. The simulation results on two case studies are reported. The first one verifies an introduced gas loss formula and another one is devoted to a modified version of IEEE 14-bus test system. The results are compared with PSO, Genetic Algorithm (GA), and Differential Evolution (DE) technique. They show that a hybrid system can operate at a lower cost than independently-operated systems; CHP units can supply more electrical demand than the electrical network while their contribution in supplying the heat demand may be smaller than that of gas furnaces. In a hybrid system, energy efficiency can be reached, producing electrical power and heat locally; CHP units have a great role in reduction of operational cost; In the coupled mode, electrical power losses are decreased, while the gas losses are increased; The simple proposed gas loss formula can provide acceptable results; Cost reductions due to employing CHPs and applying the proposed optimization technique are greater than the other methods.

A hybrid Jaya algorithm for reliability–redundancy allocation problems

ABSTRACTThis article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.

Hybrid Gravitational Search Algorithm-Particle Swarm Optimization with Time Varying Acceleration Coefficients for large scale CHPED problem

This paper proposes a novel optimization algorithm, namely hybrid Time Varying Acceleration Coefficients-Gravitational Search Algorithm-Particle Swarm Optimization (hybrid TVAC-GSA-PSO), to solve the large-scale, highly nonlinear, non-convex, non-smooth, non-differential, non-continuous, and complex Combined Heat and Power Economic Dispatch (CHPED) problems. The suggested algorithm is based on the Newtonian laws of gravitation and motion as well as swarm behaviors. The proposed technique combines the best performances of three optimization methods (i.e. GSA, TVAC-GSA, and TVAC-PSO) in terms of their specific strategies in particle movements through a self-adoptive learning strategy. The effectiveness and robustness of the suggested algorithm are tested on a set of five benchmark functions and two large-scale CHPED problems (considering valve-point loading effect and prohibited zones of conventional power units, transmission losses, as well as special characteristic of CHP units). The obtained results by the suggested algorithm in terms of quality solution, computational performance, and convergence characteristic are compared with various algorithms to show the ability of the proposed approach and its robustness in finding a better fuel cost with a less expensive solution.

Hybrid binary wolf pack algorithm for the 0-1 multidimensional knapsack problem

Wolf Pack Algorithm (WPA) has been successfully applied in unconstrained global optimisation problems. In order to effectively solve the multi-constrained combinatorial optimisation problem such as Multidimensional Knapsack Problem (MKP), a hybrid repair operator based on its specific knowledge is designed to repair and improve the infeasible solutions. Moreover, inspired by the reproductive modes of wolf pack, with the help of binary code, a Hybrid Binary Wolf Pack Algorithm (HBWPA) is proposed. Then HBWPA was tested on problems from the OR-Library to validate and demonstrate the efficiency of the proposed algorithm. The results were compared with those obtained by other two start-of-the-art existing algorithms, modified binary particle swarm optimisation and chaotic binary swarm optimisation with time-varying acceleration coefficients. Experimental results show that the proposed algorithm not only found comparable solutions for the standard MKP of different size, but also has good computational robustness.

Hybrid binary wolf pack algorithm for the 0-1 multidimensional knapsack problem

Wolf Pack Algorithm (WPA) has been successfully applied in unconstrained global optimisation problems. In order to effectively solve the multi-constrained combinatorial optimisation problem such as Multidimensional Knapsack Problem (MKP), a hybrid repair operator based on its specific knowledge is designed to repair and improve the infeasible solutions. Moreover, inspired by the reproductive modes of wolf pack, with the help of binary code, a Hybrid Binary Wolf Pack Algorithm (HBWPA) is proposed. Then HBWPA was tested on problems from the OR-Library to validate and demonstrate the efficiency of the proposed algorithm. The results were compared with those obtained by other two start-of-the-art existing algorithms, modified binary particle swarm optimisation and chaotic binary swarm optimisation with time-varying acceleration coefficients. Experimental results show that the proposed algorithm not only found comparable solutions for the standard MKP of different size, but also has good computational robustness.

Combined Heat and Power Dispatch using Hybrid Genetic Algorithm and Biogeography-based Optimization

This paper explores the performance of biogeography-based optimization (BBO) algorithm for solving combined heat and power dispatch (CHPD) or cogeneration problem of power system. BBO is a type of evolutionary algorithm which is based on the theory of biogeography and is inspired by the two concepts, namely migration of species between “islands” via flotsam, wind, flying, swimming, etc. and mutation. To improve the convergence property and solution quality, blended crossover strategy of genetic algorithm (GA) is integrated with conventional BBO algorithm in this study. The effect of valve-point in cost function is considered by adding an absolute sinusoidal term with the conventional polynomial cost function. The potential of the proposed BBO and GA based BBO (GABBO) algorithms are assessed by means of an extensive comparative study of the solutions obtained for small and medium CHPD problems of power systems. To show the priority of the proposed algorithm, comparative studies are carried out to examine the effectiveness of the proposed BBO and GABBO approaches over evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO) and time varying acceleration coefficients PSO (TVAC-PSO reported in the literature. The experimental results and comparison with other algorithms demonstrate that the proposed GABBO algorithm can be a proficient substitute lying on solving combined heat and power dispatch problems.

Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules

Estimating circuit model parameters of PV cells/modules represents a challenging problem. PV cell/module parameter estimation problem is typically translated into an optimisation problem and is solved by metaheuristic optimisation problems. Particle swarm optimisation (PSO) is considered as a popular and well-established optimisation algorithm. Despite all its advantages, PSO suffers from premature convergence problem meaning that it may get trapped in local optima. Personal and social acceleration coefficients are two control parameters that, due to their effect on explorative and exploitative capabilities, play important roles in computational behavior of PSO. In this paper, in an attempt toward premature convergence mitigation in PSO, its personal acceleration coefficient is decreased during the course of run, while its social acceleration coefficient is increased. In this way, an appropriate tradeoff between explorative and exploitative capabilities of PSO is established during the course of run and premature convergence problem is significantly mitigated. The results vividly show that in parameter estimation of PV cells and modules, the proposed time varying acceleration coefficients PSO (TVACPSO) offers more accurate parameters than conventional PSO, teaching learning-based optimisation (TLBO) algorithm, imperialistic competitive algorithm (ICA), grey wolf optimisation (GWO), water cycle algorithm (WCA), pattern search (PS) and Newton algorithm. For validation of the proposed methodology, parameter estimation has been done both for RTC France PV cell and Photowatt-PWP 201 PV module. In all terms of mean, best and standard deviation of achieved results, the proposed TVACPSO outperforms other compared algorithms.

Forecasting Range Volatility Using Support Vector Machines with Improved PSO Algorithms

Financial volatility forecasting is an important content in financial derivatives pricing, financial risk management, portfolio allocation. Support vector machines (SVM) has been successfully used in modeling and forecasting volatility. To enhance the volatility forecasting ability of SVM, this paper introduced three improved particle swarm optimization (IPSO) algorithms into SVM for forecasting range volatility. SVM was used to construct the volatility forecasting model. And, the three IPSO algorithms: stochastic inertia weight PSO (SIWPSO), time-varying acceleration coefficients PSO (TVACPSO) and two-order oscillating particle swarm optimization (TOOPSO) were applied to finding the optimal parameters in SVM, respectively. The accuracy of the proposed models in forecasting range volatility was demonstrated by using three stock indices in China stock market. The empirical results imply that compared to SVM with SIWPSO and TVACPSO algorithms, SVM with TOOPSO algorithm produces higher accuracy in forecasting range volatility and faster speed on searching for the optimal parameters of SVM.

Optimal operation of multicarrier energy systems using Time Varying Acceleration Coefficient Gravitational Search Algorithm

This paper describes a novel modified optimization algorithm based on a new heuristic method, namely Time Varying Acceleration Coefficient Gravitational Search Algorithm (TVAC-GSA), to solve both single- and multi-objective Optimal Power Flow (OPF) problems in hybrid systems especially focusing on electricity-gas network. The suggested method is based on the Newtonian laws of gravitation and motion. Sum of the complexity of both electrical and gas-based networks in terms of the valve-point loading effect of generator units, energy hub structure, energy flow equations, and different related equality and inequality constraints make the optimization problem highly nonlinear, non-convex, non-smooth, non-differential, and high-dimensional. The effectiveness of the proposed algorithm to solve such a complex problem is verified on a new introduced hybrid system based on a modified version of IEEE 14-bus network. Comparison of results obtained by the presented method with those obtained by GSA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) shows the better accuracy and fast convergence of the new method in finding an operating point with lower objective function value.

Synthesis of unequally‐spaced linear array using modified central force optimisation algorithm

In this study, a modified version of the central force optimisation (MCFO) algorithm is presented. This modified version is based on the idea of combining the ability of social thinking in particle swarm optimisation (PSO) with the search capability of the original CFO, along with the addition of time-varying acceleration coefficients, to effectively control the global search and enhance the CFO convergence capability. The convergence capability of the MCFO approach is compared with that of other recent evolutionary-based algorithms, using 12 benchmark functions grouped into unimodal and multimodal functions. Furthermore, the MCFO algorithm is considered for the synthesis of unequally-spaced linear arrays with minimum sidelobe levels and/or null placement in certain directions as well as a specified maximum null-to-null beamwidth. The comparison of the simulations of different algorithms shows that the MCFO technique is superior to other evolutionary algorithms such as the genetic algorithm, ant colony optimisation, PSO algorithm, and gravitational search algorithm, as well as other improved CFO algorithms.

Research optimization on logistics distribution center location based on adaptive particle swarm algorithm

Logistics distribution center location is an important task in the network optimization of a logistics system. A novel approach of adaptive particle swarm optimization (APSO) algorithm with nonlinear inertia weight and time-varying acceleration coefficient is proposed to achieve distribution center location problem. Mathematic model of logistics distribution center location is established according to assumptions and constraints. Then, average particle spacing as guidance factor of population diversity is employed to establish nonlinear inertia weight and arccosine function is adopted to design nonlinear symmetric acceleration coefficient in order to control the balance of global search and local search for distribution center location problem. To demonstrate the practicality and effectiveness of the proposed algorithm, an application example is provided. Experiments show that APSO can slove the location problem of distribution center with better performance..

An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization

Many organizations recognize the necessities of utilizing sophisticated tools and systems to protect their computer networks and reduce the risk of compromising their information. Although many machine-learning-based data classification algorithm has been proposed in network intrusion detection problem, each of them has its own strengths and weaknesses. In this paper, we propose an effective intrusion detection framework by using a new adaptive, robust, precise optimization method, namely, time-varying chaos particle swarm optimization (TVCPSO) to simultaneously do parameter setting and feature selection for multiple criteria linear programming (MCLP) and support vector machine (SVM). In the proposed methods, a weighted objective function is provided, which takes into account trade-off between the maximizing the detection rate and minimizing the false alarm rate, along with considering the number of features. Furthermore, to make the particle swarm optimization algorithm faster in searching the optimum and avoid the search being trapped in local optimum, chaotic concept is adopted in PSO and time varying inertia weight and time varying acceleration coefficient is introduced. The performance of proposed methods has been evaluated by conducting experiments with the NSL-KDD dataset, which is derived and modified from well-known KDD cup 99 data sets. The empirical results show that the proposed method performs better in terms of having a high detection rate and a low false alarm rate when compared with the obtained results using all features.

Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients

To develop an automatic magnetic resonance (MR) brain classification that can assist physicians to make a diagnosis and reduce wrong decisions. This article investigated the binary particle swarm optimization (BPSO) approach and proposed its three new variants: BPSO with mutation and time-varying acceleration coefficients (BPSO-MT), BPSO with mutation (BPSO-M), and BPSO with time-varying acceleration coefficients (BPSO-T). We first extracted wavelet entropy (WE) features from both approximation and detail sub-bands of eight-level decomposition. Afterwards, we used the proposed BPSO-M, BPSO-T, and BPSO-MT to select features. Finally, the selected features were fed into a probabilistic neural network (PNN). The proposed BPSO-MT performed better than BPSO-T and BPSO-M. It finally selected two features of entropies of the following two sub-bands (V1, D1). The proposed system "WE + BPSO-MT + PNN" yielded perfect classification on Data160 and Data66. In addition, it yielded 99.53% average accuracy for the Data255, over 10 repetitions of k-fold stratified cross validation (SCV), higher than state-of-the-art approaches. The proposed method is effective for MR brain classification.

Investigating supply chain performance under game theory framework using intelligent particle swarm optimisation

Game theory has been extensively used for analysis of situations comprising of multi-agents and their strategies. Supply chain can be defined as a network of multi-organisations interacting with each other during the decision analysis. Therefore, this paper exploits the salient features of game theory in mathematically modelling a supply chain problem and investigating its functioning under various alliances among partners of the same stage. The proposed structure of supply chain considers four different stages in the illustrative example. Profit of an individual partner at each stage while satisfying the constraints is considered in the objective function. In addition, transportation cost and facility utilisation within the whole supply chain are also targeted. Normalised values of different objectives are combined to formulate a multi-objective optimisation problem. This paper introduces a novel intelligent particle swarm optimisation algorithm which is embedded with two beneficial attributes viz.: 1) normal distribution in traditional particle swarm optimisation; 2) time varying acceleration coefficients. The computational experiment finds that maximum profit is gained when players are in union. It is also evident from results that the proposed algorithm outperforms over other variants of algorithm for the underlying problem thereby authenticating its superiority.