Search

Abstract: The conventional filtered-x least mean square (FxLMS) algorithm commonly employed for active noise control (ANC) is sensitive to disturbances acquired by the error microphone and yields poor performance in such scenario. To circumvent this problem, in this paper, a Wilcoxon FxLMS (WFxLMS) algorithm is proposed and used in the design of an efficient ANC which is robust to outliers in the secondary path and immune to burst noise acquired by the error microphone. It is demonstrated through simulation study that under such situation the proposed algorithm outperforms the traditional FxLMS algorithm. A particle swarm optimization (PSO) algorithm based robust ANC system, which does not require the modeling of the secondary path is also derived in the paper. Improved performance of the robust evolutionary ANC system over L2 norm based evolutionary ANC system is also shown. [Copyright &y& Elsevier]

Abstract: This paper aims at probing into the topic of power units’ operation and dispatch based on Carbon Dioxide (CO2) trading scheme. The trading cost of CO2 emission is embedded into the traditional economic dispatch model, which will be solved by the New Particle Swarm Optimization (NPSO). By considering the CO2 trading scheme, the influences of the various strategies for unit’s dispatch are simulated and analyzed in this paper. The proposed method, NPSO is developed in such a way that PSO with Constriction Factor (PSO-CF) algorithm is applied as a based level search. NPSO introduces two operators, “Random Particles” and “Fine-Tuning” into the PSO-CF algorithm to improve the drawback of searching global optimum and make the search method more efficient at the end of search. The efficiency and ability of NPSO is demonstrated by the six generating units. Simulation results indicated that reasonable solutions provide a practical and flexible framework for power sectors. They can be also used for generating alternatives and thus help decision makers to obtain the goals of minimal operation cost under their desired emission’s policies. [Copyright &y& Elsevier]

Abstract: In this paper, a new approach to the optimal control with constraints is proposed to achieve a desired end product quality for nonlinear processes based on new kernel extreme learning machine (KELM). The contributions of the paper are as follows: (1) In existing ILC algorithm, the model was built only between manipulated input variables U and output variables Y without considering the state variables. However, the states variables X state are important in the industrial processes, which are usually constrained. In this paper, the variables are divided into state variables X state , manipulated input variables U and output Y in the process of modeling. Then ΔU can be obtained by batch-to-batch iterative learning control separately. Kernel algorithm is added to ELM. (2) Constraints of state variables X state and the input variables U are considered in the current version. PSO is used to solve the optimization problem. (3) Kernel trick is introduced to improve accuracy of ELM modeling. New KELM algorithm is proposed in the current version. The input trajectory for the next batch is accommodated by searching for the optimal value through the error feedback at a minimum cost. The particle swarm optimization algorithm is used to search for the optimal value based on the iterative learning control (ILC). The proposed approach has been shown to be effective and feasible by applying bulk polymerization of the styrene batch process and fused magnesium furnace. [Copyright &y& Elsevier]

Abstract: In this paper, Java programming with applets for internet-based control education of two mechanical systems are presented. First, a new multi-objective optimization method is applied to obtain the Pareto frontiers of some non-commensurable objective functions in the design of linear state feedback controllers for an inverted pendulum and a ball–beam system. Second, the simulations of the problems were developed with Java applets and its results are given. The obtained results and analyses demonstrate that this multi-objective method presented in this paper operates very well in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel method for solving the Non-convex Economic Dispatch (NED) problems, by the Fuzzy Adaptive Modified Particle Swarm Optimization (FAMPSO). Practical ED problems have non-smooth cost functions with equality and inequality constraints when generator valve-point loading effects are taken into account. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. PSO is one of modern heuristic algorithms, in which particles change place to get close to the best position and find the global minimum point. However, the classic PSO may converge to a local optimum solution and the performance of the PSO highly depends on the internal parameters. To overcome these drawbacks, in this paper, a new mutation is proposed to improve the global searching capability and prevent the convergence to local minima. Also, a fuzzy system is used to tune its parameters such as inertia weight and learning factors. In order to evaluate the performance of the proposed algorithm, it is applied to a system consisting of 13 and 40 thermal units whose fuel cost function is calculated by taking account of the effect of valve-point loading. Simulation results demonstrate the superiority of the proposed algorithm compared to other optimization algorithms presented in literature. [Copyright &y& Elsevier]

Abstract: In this paper, a powerful evolutional particle swarm optimization (PSO) learning algorithm is developed that self-generates radial basis function neural networks (RBFNs) to deal with three nonlinear problems. In general, the simple but efficient PSO learning algorithm is adapted for solving complex and global optimization problems. With computer simulation, this paper illustrates, in detail, how the PSO algorithm can automatically tune the centers and spreads of each radial basis function, and the connection weights. Furthermore, in parallel with the free RBFNs parameters, the number of radial basis functions of the constructed RBFNs can be automatically minimized by choosing a special fitness function which takes this factor into account. Therefore, the proposed PSO allows high accuracy within a short training time when determining RBFNs with small numbers of radial basis functions. Simulation results demonstrate the proposed RBFNs’ efficiency to stabilize an inverted pendulum, approximate a nonlinear function and approximate a discrete dynamic system. [Copyright &y& Elsevier]

This paper explains the development of a new algorithm for maximum power point tracking (MPPT) in large PV systems under partial shading conditions (PSC). The new algorithm combines the use of particle swarm optimization (PSO) for MPPT during the initial stages of tracking and then employs the traditional perturb and observe (PO) method at the final stages. The methodology has been first simulated in two different PV configurations under varying shading patterns and experimentally verified using a microcontroller based experimental system. The integration of swarm intelligence with PO algorithm is shown to yield faster convergence to the global maximum power point (GMPP) than when the two methods are individually used. The oscillations in the output power, voltage and current of the PV system with the proposed method are the least when compared to the ones obtained during PSO based MPPT. [ABSTRACT FROM AUTHOR]

Abstract: Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced to initialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjust the network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods. [Copyright &y& Elsevier]

Abstract: During the design, maintenance and decommissioning of nuclear facilities, nuclear radiation protection is an important part. In recent years, researchers have explored a lot of radiation protection approach, and some radiation protection approach have been applied in practice, such as visualization technique of radiation environment, path-planning method, robotics and etc., in these techniques the path-planning in radiation environment technology has become an important radiation protection measure. In this paper, we addressed a staff walking path-planning approach in radiation environment based on particle swarm algorithm and introduced some key technologies of path-planning in radiation environment. To obtain the optimal walking route to verify the operation of the proposed method, we carried out the simulation experiment in which dose and distance were as decision factors. The experiment results represented the probability and the effectiveness of path-planning in radiation environment based on particle swarm algorithm. [Copyright &y& Elsevier]

Abstract: Economic environment dispatch (EED) is a significant optimization problem in conventional fossil fuel fired power system to distribute load demand reasonably and scientifically so that fuel cost and emission issues are optimized simultaneously while satisfying various constraints. Valve-point effect, prohibited operation zones (POZs) and transmission losses make the EED a non-smooth and non-convex optimization problem. In order to solve this constrained multi-objective optimization problem including competing objectives as well as complex constraints, an enhanced multi-objective cultural algorithm (EMOCA) is proposed in this paper. The proposed method combines cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Besides, two knowledge structures of belief space in CA are redefined according to the features of EED problem. Moreover, an effective constrain handling method is proposed to handle the equality and inequality constraints of EED problem. Simulation results of test systems demonstrate the capability of the proposed algorithm to generate well-distributed Pareto optimal solutions in a single run. Compared with some current methods, EMOCA has a good performance in finding a diverse set of solutions and in converging near the true Pareto optimal front with lower fuel cost and emission issues synthetically. [Copyright &y& Elsevier]

Abstract: This paper analyzes the alternative of a hybrid active power filter (HAPF) connected to the medium-voltage (10kV) distribution network to enhance the power quality. It proposes a multi-objective optimization algorithm based on improved particle swarm optimization (PSO) to design and optimize the main circuit of HAPF. On the basis of theoretical analysis, it removes engineering experience and redundant constraints from the optimization algorithm, and establishes a unified description of performance for HAPF. To avoid premature of PSO algorithm, an adaptive inertial weight based on sigmoid function is employed to improve the diversity of the solution space. The simulation and experimental results show that the proposed design and optimization of main circuit for HAPF is correct and effective. [Copyright &y& Elsevier]

Abstract: There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques. [Copyright &y& Elsevier]

Abstract: In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency. [Copyright &y& Elsevier]

Abstract: This paper proposes a new multiobjective evolutionary algorithm (MOEA) by extending the existing cat swarm optimization (CSO). It finds the nondominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. The performance of our proposed approach is demonstrated using standard test functions. A quantitative assessment of the proposed approach and the sensitivity test of different parameters is carried out using several performance metrics. The simulation results reveal that the proposed approach can be a better candidate for solving multiobjective problems (MOPs). [Copyright &y& Elsevier]

Abstract: In this paper, a new approach is proposed to solve the economic load dispatch (ELD) problem. Power generation, spinning reserve and emission costs are simultaneously considered in the objective function of the proposed ELD problem. In this condition, if the valve-point effects of thermal units are considered in the proposed emission, reserve and economic load dispatch (ERELD) problem, a non-smooth and non-convex cost function will be obtained. Frequency deviation, minimum frequency limits and other practical constraints are also considered in this problem. For this purpose, ramp rate limit, transmission line losses, maximum emission limit for specific power plants or total power system, prohibited operating zones and frequency constraints are considered in the optimization problem. A hybrid method that combines the bacterial foraging (BF) algorithm with the Nelder–Mead (NM) method (called BF–NM algorithm) is used to solve the problem. In this study, the performance of the proposed BF–NM algorithm is compared with the performance of other classic (non-linear programming) and intelligent algorithms such as particle swarm optimization (PSO) as well as genetic algorithm (GA), differential evolution (DE) and BF algorithms. The simulation results show the advantages of the proposed method for reducing the total cost of the system. [Copyright &y& Elsevier]

Abstract: This paper presents a particle swarm optimization with differentially perturbed velocity hybrid algorithm with adaptive acceleration coefficient (APSO-DV) for solving the optimal power flow problem with non-smooth and non-convex generator fuel cost characteristics. The APSO-DV employs differentially perturbed velocity with adaptive acceleration coefficient for updating the positions of particles for the particle swarm optimization. The feasibility of the proposed approach was tested on IEEE 30-bus and IEEE 118-bus systems with three different objective functions. Several cases were investigated to test and validate the robustness of the proposed method in finding the optimal solution. The effectiveness of the proposed approach was tested including contingency also. Simulation results demonstrate that the APSO-DV provides superior results compared to classical DE, PSO, PSO-DV and other methods recently reported in the literature. An innovative statistical analysis based on central tendency measures and dispersion measures was carried out on the bus voltage profiles and voltage stability indices. [Copyright &y& Elsevier]

Abstract: This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems. [Copyright &y& Elsevier]

Abstract: The main idea in distribution network reconfiguration is usually to reduce loss by changing the status of sectionalizing switches and determining appropriate tie switches. Recently Distribution FACTS (DFACTS) devices such as DSTATCOM also have been planned for loss reduction and voltage profile improvement in steady state conditions. This paper implements a combinatorial process based on reconfiguration and DSTATCOM allocation in order to mitigate losses and improve voltage profile in power distribution networks. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. Differential evolution algorithm (DEA) has been used to solve and overcome the complicity of this combinatorial nonlinear optimization problem. To validate the accuracy of results a comparison with particle swarm optimization (PSO) has been made. Simulations have been applied on 69 and 83 busses distribution test systems. All optimization results show the effectiveness of the combinatorial approach in loss reduction and voltage profile improvement. [Copyright &y& Elsevier]

Abstract: In this paper, a closed-loop fuzzy logic controller based on the particle swarm optimization algorithm is proposed for controlling the power level of nuclear research reactors. The principle of the fuzzy logic controller is based on the rules constructed from numerical experiments made by means of a computer code for the core dynamics calculation and from human operator''s experience and knowledge. In addition to these intuitive and experimental design efforts, consequent parts of the fuzzy rules are optimally (or near optimally) determined using the particle swarm optimization algorithm. The contribution of the proposed algorithm to a reactor control system is investigated in details. The performance of the controller is also tested with numerical simulations in numerous operating conditions from various initial power levels to desired power levels, as well as under disturbance. It is shown that the proposed control system performs satisfactorily under almost all operating conditions, even in the case of very small initial power levels. [Copyright &y& Elsevier]

Abstract: In recent years, accurate identification of voltage versus current (V–I) characteristics of proton exchange membrane fuel cell (PEMFC) has attracted significant attention in the literature. However, the main drawback in accurate modeling is the lack of information about the precise values of the model parameters. In this paper, in order to overcome this drawback a grouping-based global harmony search algorithm, named GGHS, is proposed for parameter identification issue. The proposed algorithm attempts to provide an efficient way in which a new harmony can be properly improvised. In order to study the capability of the proposed algorithm, the results obtained by GGHS are compared with those obtained by two versions of harmony search (HS) algorithms, three versions of particle swarm optimization (PSO) algorithms, as well as seeker optimization algorithm (SOA). Simulation results accentuate the superiority of the GGHS over the other methods. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO’s convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle (aGB) that has the potential to be a better “guide” than the PSO’s native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension. [Copyright &y& Elsevier]

Abstract: As a powerful optimization algorithm, particle swarm optimization (PSO) has been widely applied to power system researches. However, most existing applications of PSO can only be implemented offline. The difficulties of online implementation mainly come from the unavoidable lengthy simulation time to evaluate a candidate solution. Recently, PSO was implemented online that can identify parameters in a motor control systems. In this paper, the real-time PSO (RT-PSO) based identification technique is applied to cancel current harmonics in power systems. By transforming the identification problem to optimization problem, RT-PSO can simultaneously identify four parameters associated with fundamental current from measurement. In this way, there is no need to identify the fundamental frequency separately or construct fundamental signal from identified harmonic information. The identification algorithm can be applied to three-phases independently, even for unbalanced system or single-phase system. The identified fundamental signal is then used as the reference for current harmonics cancellation. The RT-PSO based harmonic cancellation is realized with an active filter and used to compensate harmonic current created by a nonlinear load. Simulation results demonstrate that the RT-PSO algorithm can provide accurate identification of the fundamental current which in turn will result in good harmonic cancellation performance. As a capable online optimization technique, RT-PSO can be extensively applied to many optimization and control problems. [ABSTRACT FROM AUTHOR]

Abstract: QoS multicast routing in networks is a very important research issue in networks and distributed systems. It is also a challenging and hard problem for high-performance networks of the next generation. Due to its NP-completeness, many heuristic methods have been employed to solve the problem. This paper proposes the modified quantum-behaved particle swarm optimization (QPSO) method for QoS multicast routing. In the proposed method, QoS multicast routing is converted into an integer programming problem with QoS constraints and is solved by the QPSO algorithm combined with loop deletion operation. The QPSO-based routing method, along with the routing algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA), is tested on randomly generated network topologies for the purpose of performance evaluation. The simulation results show the efficiency of the proposed method on QoS the routing problem and its superiority to the methods based on PSO and GA. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new approach for the detection and classification of single and combined power quality (PQ) disturbances is proposed using fuzzy logic and a particle swarm optimization (PSO) algorithm. In the proposed method, suitable features of the waveform of the PQ disturbance are first extracted. These features are extracted from parameters derived from the Fourier and wavelet transforms of the signal. Then, the proposed fuzzy system classifies the type of PQ disturbances based on these features. The PSO algorithm is used to accurately determine the membership function parameters for the fuzzy systems. To test the proposed approach, the waveforms of the PQ disturbances were assumed to be in the sampled form. The impulse, interruption, swell, sag, notch, transient, harmonic, and flicker are considered as single disturbances for the voltage signal. In addition, eight possible combinations of single disturbances are considered as the PQ combined types. The capability of the proposed approach to identify these PQ disturbances is also investigated, when white Gaussian noise, with various signal to noise ratio (SNR) values, is added to the waveforms. The simulation results show that the average rate of correct identification is about 96% for different single and combined PQ disturbances under noisy conditions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new method named MSSE-PSO (master–slave swarms shuffling evolution algorithm based on particle swarm optimization) is proposed. Firstly, a population of points is sampled randomly from the feasible space, and then partitioned into several sub-swarms (one master swarm and other slave swarms). Each slave swarm independently executes PSO or its variants, including the update of particles’ position and velocity. For the master swarm, the particles enhance themselves based on the social knowledge of master swarm and that of slave swarms. At periodic stage in the evolution, the master swarm and the whole slave swarms are forced to mix, and points are then reassigned to several sub-swarms to ensure the share of information. The process is repeated until a user-defined stopping criterion is reached. The tests of numerical simulation and the case study on hydrological model show that MSSE-PSO remarkably improves the accuracy of calibration, reduces the time of computation and enhances the performance of stability. Therefore, it is an effective and efficient global optimization method. [Copyright &y& Elsevier]

Abstract: Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm Optimization (APSO) is proposed to increase the convergence speed and accuracy of the basic particle swarm optimization to save tremendous computation time. An illustrative example for the modeling of bilinear systems is provided to confirm the validity, as compared with the Genetic Algorithm (GA), Linearly Decreasing Inertia Weight PSO (LDW-PSO), Nonlinear Inertia Weight PSO (NDW-PSO) and Dynamic Inertia Weight PSO (DIW-PSO) in terms of parameter accuracy and convergence speed. Second, APSO is also improved to detect and determine varying parameters. In this case, a sentry particle is introduced to detect any changes in system parameters. Simulation results confirm that the proposed algorithm is a good promising particle swarm optimization algorithm for online parameter estimation. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new Three-Stage (THS) approach for solving Unit Commitment (UC) problem. The proposed method has a simple procedure to get at favorite solutions in a feasible duration of time by producing a primal schedule of status of units at the first step. In the second step the operating units take hourly values by doing Economic Dispatch (ED) on them via a hybrid serial algorithm of Artificial Intelligence (AI) including Particle Swarm Optimization (PSO) and Nelder–Mead (NM) algorithms. In spite of the acceptable solutions obtained by these two stages, the presented method takes another step called the solution modification process (SMP) to reach a more suitable solution. The simulation results over some standard cases of UC problem confirm that this method produces robust solutions and generally gets appropriate near-optimal solutions. [Copyright &y& Elsevier]

Abstract: In recent years, different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing cell formation using a modified particle swarm optimization (PSO) algorithm. The main modification that this work made to the original PSO algorithm consists in not using the vector of velocities that the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining applications. Some simulation results are presented and compared with results from literature. The criterion used to group the machines into cells is based on the minimization of intercell movements. The computational results show that the PSO algorithm is able to find the optimal solutions in almost all instances, and its use in machine grouping problems is feasible. [Copyright &y& Elsevier]

Abstract: This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization is a novel swarm-intelligence-based algorithm and a valid optimization technique. However, the algorithm suffers from the premature convergence problem when facing to complex optimization problem. In order to keep the balance between the global exploration and the local exploitation validly, the paper develops a knowledge-based cooperative particle swarm optimization (KCPSO). KCPSO mainly simulates the self-cognitive and self-learning process of evolutionary agents in special environment, and introduces a knowledge billboard to record varieties of search information. Moreover, KCPSO takes advantage of multi-swarm to maintain the swarm diversity and tries to guide their evolution by the shared information. Under the guide of the shared information, KCPSO manipulates each sub-swarm to go on with local exploitation in different local area, in which every particle follows a social learning behavior mode; at the same time, KCPSO carries out the global exploration through the escaping behavior and the cooperative behavior of the particles in different sub-swarms. KCPSO can maintain appropriate swarm diversity and alleviate the premature convergence validly. The proposed model was applied to some well-known benchmarks. The relative experimental results show KCPSO is a robust global optimization method for the complex multimodal functions. [Copyright &y& Elsevier]

Abstract: In this paper, a novel quantum swarm evolutionary algorithm (QSE) is presented based on the quantum-inspired evolutionary algorithm (QEA). A new definition of Q-bit expression called quantum angle is proposed, and an improved particle swarm optimization (PSO) is employed to update the quantum angles automatically. The simulated results in solving 0–1 knapsack problem show that QSE is superior to traditional QEA. In addition, the comparison experiments show that QSE is better than many traditional heuristic algorithms, such as climb hill algorithm, simulation anneal algorithm and taboo search algorithm. Meanwhile, the experimental results of 14 cities traveling salesman problem (TSP) show that it is feasible and effective for small-scale TSPs, which indicates a promising novel approach for solving TSPs. [Copyright &y& Elsevier]