Craziness Based Particle Swarm Optimization Research Articles

System-reliability-based sizing and shape optimization of trusses considering millions of failure sequences

System-Reliability-Based Design Optimization (S-RBDO) of structures considering progressive collapse is a complex problem, as the number of potential failure sequences increases geometrically with the degree of static indeterminacy of the structure. Existing methods for identifying failure sequences in structural systems are computationally expensive and prone to missing some critical failure sequences, especially within an optimization framework. In this context, identifying the most critical failure sequences to simplify the problem is fundamental. Herein, we propose a novel system-reliability-based framework for sizing and shape optimization of trusses. The procedure identifies minimal cut sets using the recently developed null space method, which has been proven more efficient than traditional failure path-based methods. The most probable failure sequence is selected from each identified minimal cut set. System reliability is estimated using the dominant failure sequences for the whole structure, selected based on their correlations using the Probabilistic Network Evaluation Technique (PNET). Craziness-Based Particle Swarm Optimization (CRPSO) is employed as the optimization algorithm. Numerical examples involving hundreds to millions of failure sequences demonstrate applicability and efficiency of the proposed framework on truss optimization problems with different material post-failure behaviors. Results suggest that, in a system-reliability analysis considering progressive collapse, the most critical failure sequences are those obtained from minimal cut sets. Furthermore, results show that the procedure proposed herein can outperform other frameworks based on traditional failure path-based methods. Simple truss sizing and shape optimization is considered herein, but the conclusions have immediate relevance to the optimal design of realistic structures considering progressive collapse.

Optimal pattern synthesis of circular antenna arrays with improved effective aperture and beam area

Abstract Circular antenna array (CAA) is one of the most widely used antenna array designs. This paper addresses the design challenges of the CAA with the non-uniform single ring, which is placed in an X-Y plane with the best sidelobe level (SLL) and improved first null beamwidth (FNBW). It has been solved using differential evolution, craziness-based particle swarm optimization (CRPSO), and novel particle swarm optimization (NPSO) techniques. An optimal combination of feeding current and inter-element spacing provides an array pattern with the best SLL and improved FNBW, as well as some other parameter calculations of the antenna array like maximum directivity, maximum effective aperture, total effective aperture, maximum beam area, total beam area, circumference, and radius of the CAAs using these techniques. There are six designs of CAAs with different antenna elements (i.e., 10-, 12-, 16-, 20-, 36-, and 64-elements) which have been taken into account. Simulations are done in MATLAB. Based on various simulation results, we can analyze the performance of SLL and FNBW with other parameters using NPSO and compare them with different techniques of CAAs, as shown in the numerical analysis and simulation result section.

Optimal Load Frequency Control of Island Microgrids via a PID Controller in the Presence of Wind Turbine and PV

This article studied the load frequency control (LFC) of a multi-source microgrid with the presence of renewable energy sources. To maintain a sustainable power supply, the frequency of the system must be kept constant. A Proportional–Integral–Derivative (PID) controller is presented as a secondary controller to control the frequency of the microgrid in island mode, and the integral of squared time multiplied by error squared (ISTES) is used as a performance index. The use of the Craziness-Based Particle Swarm Optimization (CRPSO), which is an improved version of Particle Swarm Optimization (PSO), improves the convergence speed in optimizing the nonlinear problem of load and frequency controller design. The test microgrid is composed of the load and distributed generation units such as diesel generators, photovoltaics and wind turbines. The proposed controller provided the desired response to adjusting the microgrid frequency, achieving the final response after a short time and making it more stable and less oscillatory compared with the conventional system.

Digital FIR Filter Design by PSO and its v ariants Attractive and Repulsive PSO(ARPSO) & Craziness based PSO(CRPSO)

Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.

Optimal placement of DG units for the enhancement of MG networks performance using coalition game theory

Nowadays, the continuous growth of energy demand micro-grid (MG) networks that use as distributed energy source. MGs are usually installed at locations, where access to the power grid is not economical due to the far distance and losses. To cost reduction in MG networks, it is necessary to optimize placement of distributed generation (DG) units in MG networks. In this study, the optimal allocation of DG units using craziness-based particle swarm optimization (CRPSO) algorithm based on the game-theoretic formulation strategy was proposed to decrease power supply costs. The objective function of optimization comprises of the cost of buying power, power loss, communication and load shedding. Load shedding is considered so that the cost of operation of MGs declined. The optimization includes the size, site and the optimal order of joined DG units (wind turbine and photovoltaic) in test system. The novelty of this research is that it determines the order of joined DG units in the coalition when the arrangement of seller MG after DG unit's placement is reshaped. The simulation results in the modified IEEE 33 buses system validated through the MATLAB. The simulation results show the reduction in the costs of operation and rise in the sellers' profit.

Optimal design of complementary metal-oxide-semiconductor analogue circuits: An evolutionary approach

This article proposes an efficient approach to design two different CMOS analogue circuits, namely p-channel Metal-Oxide-Semiconductor (PMOS) driver-based two-stage comparator and n-channel Metal-Oxide-Semiconductor (NMOS) driver-based folded-cascode operational amplifier, using a modified version of particle swarm optimization (PSO) known as craziness-based PSO (CRPSO) algorithm. PSO replicates the behaviour of bird flocking, and it requires a fewer number of control parameters. The drawbacks of PSO are premature convergence and stagnation problem. Due to these drawbacks, the PSO is improved to CRPSO for the optimal design of analogue circuits. The near-global optimal solution has been achieved by using the CRPSO. The CRPSO is used to attain the optimal sizes of the MOS devices for the individual circuits, which lead to the minimization of the total MOS area occupied by the circuit. SPICE simulation has also been carried out with the optimal device dimensions achieved from the CRPSO algorithm. Statistical analysis and the process corner analysis have also been performed for the above mentioned circuits. SPICE-based results confirm that the CRPSO-based approach is a better technique compared to the other earlier reported methods for both the circuits.

Microgrid utilization by optimal allocation of DG units: Game theory coalition formulation strategy and uncertainty in renewable energy resources

The continuous growth of electricity demands microgrid distribution networks that use distributed energy sources. Therefore, the necessity for applying efficient methods to improve the performance of microgrids as a part of the power industry is felt more and more day by day. Microgrids are usually installed at locations where access to the power grid is not economical due to the far distance and losses. In this paper, the optimal allocation of distributed generation (DG) units using the craziness-based particle swarm optimization (CRPSO) algorithm based on the game theoretic formulation strategy was proposed to reduce the exchange of power between the macrostation and microgrids. The main motive for presenting this research is the combination of the advantages of coalition game theory, DG units and the CRPSO optimization algorithm. The advantages have caused the load of the microgrids to be implemented as much as possible through the exchanges between the microgrids and the cost of utilization and power supply of the loads minimized. Uncertainties in wind speed and solar radiation flux are also considered for the purpose of applying the random property of the distributed generation resources. The simulation results on the 33rd IEEE standard system in different scenarios indicate the desired performance of the proposed method.

Evolutionary Computation Based Sizing Technique of Nulling Resistor Compensation Based CMOS Two-Stage Op-Amp Circuit

This article explores the comparative optimizing efficiency between two PSO variants, namely, Craziness based PSO (CRPSO) and PSO with an Aging Leader and Challengers (ALC-PSO) for the design of nulling resistor compensation based CMOS two-stage op-amp circuit. The concept of PSO is simple and it replicates the nature of bird flocking. As compared with Genetic algorithm (GA), PSO deals with less mathematical operators. Premature convergence and stagnation problem are the two major limitations of PSO technique. CRPSO and ALC-PSO techniques individually have eliminated the disadvantages of the PSO technique. In this article, CRPSO and ALC-PSO are individually employed to optimize the sizes of the MOS transistors to reduce the overall area taken by the circuit while satisfying the design constraints. The results obtained individually from CRPSO and ALC-PSO techniques are validated in SPICE environment. SPICE based simulation results justify that ALC-PSO is much better technique than CRPSO and other formerly reported methods for the design of the afore mentioned circuit in terms of the MOS area, gain and power dissipation etc.

Swarm Intelligence-based Optimal Design to Minimize the Sidelobe Level of Concentric Circular Antenna Array with Element Failures

ABSTRACTIn an antenna array due to large number of elements, there is always a possibility of failure of one or more elements in the array system, which results in increased SLL with distorted radiation pattern. Recovering the optimal beam pattern can be done by recalculating the amplitude distributions for array elements leaving the failed elements. Here, we have corrected the radiation pattern for concentric circular antenna array (CCAA) with central element, as CCAA with central element is widely implemented in smart antennas and mobile communication. A central element has been added to array to increase the steering capability of proposed array. It has been noticed that after failure of three or more number of elements with central element, there is a significant increase in side lobe level (SLL) which degrades the performance of the system. This distortion is corrected using bacterial foraging optimization, standard particle swarm optimization (PSO), and craziness-based particle swarm optimization (CRPSO) technique. The results of standard PSO and CRPSO are compared for a three ring CCAA with central element and it has been concluded that CRPSO gives the optimum result.

Truss optimization with multiple frequency constraints and automatic member grouping

This paper deals with sizing and shape structural optimization problems with respect to the minimization of the masses of truss structures considering multiple natural frequencies as the constraints of the problems. The sizing and shape design variables are discrete and continuous, respectively. It can be attractive to use a reduced number of distinct cross-sectional areas minimizing costs of fabrication, transportation, storing, checking, welding, and so on. Also, it is expected a labor-saving when the structure is welded, checked and so on. On the other hand, one can observe that the task of discovering the optimum member grouping is not trivial and leads to an exhaustive trial-and-error process. Cardinality constraints are adopted in order to obtain an automatic variable linking searching for the best member grouping of the bars of the trusses analyzed in this paper. A CRPSO (Craziness based Particle Swarm Optimization) is the search algorithm adopted in this paper. This algorithm uses a modified velocity expression and an operator called “craziness velocity” in order to avoid premature convergence. An Adaptive Penalty Method is adopted to handle the constraints. Six truss structures are analyzed, presenting very interesting results providing curves of tradeoff between the optimized weights versus the number of distinct cross-sectional areas used in these solutions.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.

Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.

An adaptive penalty scheme to solve constrained structural optimization problems by a Craziness based Particle Swarm Optimization

The use of Evolutionary Algorithms (EAs) to solve optimization problems has been increasing. One of the most used techniques is Particle Swarm Optimization (PSO), which is considered robust, efficient and competitive in comparison with other bio-inspired algorithms. EAs were originally designed to solve unconstrained optimization problems. However, the most significant problems, particularly those from real world optimization, present constraints. It is not trivial to define a strategy to handle constraints and, in general, penalty functions containing parameters to be set by the user and it may affect the search considerably. This paper consists of a combination of the Craziness based Particle Swarm Optimization (CRPSO) with an adaptive penalty technique, called Adaptive Penalty Method (APM), to solve constrained optimization problems. A CRPSO is adopted here in order to avoid premature convergence using a new velocity expression and an operator called “craziness velocity”. APM and its variants were applied in other EAs, originally in a Genetic Algorithm, which demonstrated its robustness. APM deals with inequality and equality constraints, and it is free of parameters to be defined by the user. In order to assess the applicability and performance of the algorithm, several structural engineering optimization problems traditionally found in the literature are used in the computational experiments.

Comparative performance investigation of optimal controller for AGC of electric power generating systems

In this study, an attempt is made to present the application and comparative performance analysis of optimal control approach for automatic generation control (AGC) of electric power generating systems. Optimal controller is designed utilizing performance index minimization criterion. To conduct the study, various single and multi-area models with/without system nonlinearities from the literature are simulated under sudden load perturbation. In this comparative study, to corroborate the worth of optimal controller, the performance of optimal AGC controller is compared with that of I/PI controller optimized adopting recently published the best established techniques such as teacher learning based optimization (TLBO), differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), hybrid bacteria foraging optimization algorithm-PSO (hBFOA-PSO), craziness based PSO (CBPSO), firefly algorithm (FA), krill herd algorithm (KHA), moth-flame optimization (MFO), glow swarm optimization (GSO), simulated annealing (SA), bat algorithm (BA), stochastic fractal search (SFS) and hybrid SFSlocal unimodal sampling (hSFS-LUS) technique. The simulated results are compared in terms of settling time (ST), peak undershoot (PU)/overshoot (PO), various performance indices (PIs), minimum damping ratio (ξmin) and system eigenvalues. A sensitivity study is conducted to certify the robustness of optimal controller.

AGC of a multi-area multi-source hydrothermal power system interconnected via AC/DC parallel links under deregulated environment

Abstract This paper proposes the automatic generation control (AGC) of an interconnected multi-area multi-source hydrothermal power system under deregulated environment. The two equal control areas with hydro and thermal generating power sources are interconnected via AC/DC parallel links. The optimal proportional integral (PI) regulators are designed for the proposed power system to simulate all power market transactions which are possible in a restructured power system. The concept of DISCO participation matrix (DPM) is harnessed to simulate the transactions. Eigenvalue study is conducted to assess the effect of AC/DC parallel links on system performance. The study is also conducted, considering appropriate generation rate constraints (GRCs) for thermal and hydro generating sources. Further, the dynamic responses of the proposed multi-source hydrothermal power system are compared with single-source thermal–thermal power system and it has been ascertained that the responses of proposed power system are sluggish with large overshoots and settling times. Finally, the study is extended to frame and implement optimal PI regulators for the first time for the AGC of a conventional two-area non-reheat thermal power system with governor dead-band nonlinearity. The superiority of the optimal PI regulators has been established by comparing the results with recently published best claimed craziness based particle swarm optimization (CRAZYPSO) and hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithms based PI controller tuned for the same interconnected power system.

Design and analysis of hybrid firefly algorithm-pattern search based fuzzy PID controller for LFC of multi area power systems

Abstract A hybrid Firefly Algorithm (FA) and Pattern Search (PS) optimized fuzzy PID controller is proposed for Load Frequency Control (LFC) of multi area power systems. Initially a two area thermal system with Governor Dead Band (GDB) nonlinearity is considered and the gains of the fuzzy PID controller are optimized employing a hybrid FA and PS (hFA–PS) optimization technique. The supremacy of proposed hFA–PS over FA is also demonstrated. The advantage of the proposed fuzzy PID controller has been shown by comparing the results with some recently published techniques, such as Differential Evolution (DE) and Craziness based Particle Swarm Optimization (CPSO). Further, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Additionally, the proposed approach is further extended to three unequal area thermal systems with physical constraints such as time delay, reheat turbine, Generation Rate Constraint (GRC) and GDB nonlinearity. It is observed that the proposed technique is able to handle nonlinearity and physical constraints in the system model.

Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm

AbstractThe inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds’ flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.

Optimal analog active filter design using craziness‐based particle swarm optimization algorithm

SummaryBecause of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness‐based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth‐order Butterworth low‐pass analog active filter and second‐order state variable low‐pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low‐pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd.

Differential evolution algorithm based automatic generation control for interconnected power systems with non-linearity

This paper presents the design and performance analysis of Differential Evolution (DE) algorithm based Proportional–Integral (PI) and Proportional–Integral–Derivative (PID) controllers for Automatic Generation Control (AGC) of an interconnected power system. Initially, a two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. In the proposed approach, the design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions are used for the design purpose. The superiority of the proposed approach has been shown by comparing the results with a recently published Craziness based Particle Swarm Optimization (CPSO) technique for the same interconnected power system. It is noticed that, the dynamic performance of DE optimized PI controller is better than CPSO optimized PI controllers. Additionally, controller parameters are tuned at different loading conditions so that an adaptive gain scheduling control strategy can be employed. The study is further extended to a more realistic network of two-area six unit system with different power generating units such as thermal, hydro, wind and diesel generating units considering boiler dynamics for thermal plants, Generation Rate Constraint (GRC) and Governor Dead Band (GDB) non-linearity.

Craziness based particle swarm optimization algorithm for IIR system identification problem

In this paper a variant of particle swarm optimization (PSO), called craziness based particle swarm optimization (CRPSO) technique is applied to the infinite impulse response (IIR) system identification problem. A modified version of PSO, called CRPSO adopts a number of random variables for having better and faster exploration and exploitation in multidimensional search space. Incorporation of craziness factor in the basic velocity expression of PSO not only brings diversity in particles but also ensures convergence to optimal solution. The proposed CRPSO based system identification approach has alleviated from the inherent drawbacks of premature convergence and stagnation, unlike real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CRPSO over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.