Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

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Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

The particle swarm optimization (PSO) is a recently developed evolutionary algorithm (EA) based on the swarm behavior in the nature. This paper presents recent advances in applying a versatile PSO engine to real-number, binary, single-objective and multiobjective optimizations for antenna designs, with a randomized Newtonian mechanics model developed to describe the swarm behavior. The design of aperiodic (nonuniform and thinned) antenna arrays is presented as an example for the application of the PSO engine. In particular, in order to achieve an improved peak sidelobe level (SLL), element positions in a nonuniform array are optimized by real-number PSO (RPSO). On the other hand, in a thinned array, the on/off state of each element is determined by binary PSO (BPSO). Optimizations for both nonuniform arrays and thinned arrays are also expanded to multiobjective cases. As a result, nondominated designs on the Pareto front enable one to achieve other design factors than the peak SLL. Optimized antenna arrays are compared with periodic arrays and previously presented aperiodic arrays. Selected designs fabricated and measured to validate the effectiveness of PSO in practical electromagnetic problems

Advances in Particle Swarm Optimization for Antenna Designs: Real-Number, Binary, Single-Objective and Multiobjective Implementations

Particle swarm optimisation (PSO) has been enormously successful. Within little more than a decade hundreds of papers have reported successful applications of PSO. In fact, there are so many of them, that it is difficult for PSO practitioners and researchers to have a clear up-to-date vision of what has been done in the area of PSO applications. This brief paper attempts to fill this gap, by categorising a large number of publications dealing with PSO applications stored in the IEEE Xplore database at the time of writing.

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Analysis of the publications on the applications of particle swarm optimisation

This paper describes the synthesis method of linear array geometry with minimum sidelobe level and null control using the particle swarm optimization (PSO) algorithm. The PSO algorithm is a newly discovered, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the PSO algorithm is much easier to understand and implement and requires the least of mathematical preprocessing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression and/or null placement in certain directions, and then solved by the PSO algorithm for the optimum element locations. Three design examples are presented that illustrate the use of the PSO algorithm, and the optimization goal in each example is easily achieved. The results of the PSO algorithm are validated by comparing with results obtained using the quadratic programming method (QPM).

Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization

http://www.cse.cuhk.edu.hk/~lyu/paper_pdf/sdarticle.pdf

A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training

Particle swarm optimization is a recently invented high-performance optimizer that is very easy to understand and implement. It is similar in some ways to genetic algorithms or evolutionary algorithms, but requires less computational bookkeeping and generally only a few lines of code. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for phased array synthesis of a far-field sidelobe notch, using amplitude-only, phase-only, and complex tapering. The results show that some optimization scenarios are better suited to one method versus the other (i.e., particle swarm optimization performs better in some cases while genetic algorithms perform better in others), which implies that the two methods traverse the problem hyperspace differently. The particle swarm optimizer shares the ability of the genetic algorithm to handle arbitrary nonlinear cost functions, but with a much simpler implementation it clearly demonstrates good possibilities for widespread use in electromagnetic optimization.

Particle swarm optimization versus genetic algorithms for phased array synthesis

The scan volume of a thinned periodic linear phased array is proportional to the spacing between array elements. As the spacing between elements increases beyond a half wavelength, the scan range of the array will be significantly reduced due to the appearance of grating lobes. This paper investigates a method of creating thinned aperiodic linear phased arrays through the application of genetic algorithms that will suppress the grating lobes with increased steering angles. In addition, the genetic algorithm will place restrictions on the driving-point impedance of each element so that they are well behaved during scanning. A genetic algorithm approach is also introduced for the purpose of evolving an optimal set of matching networks. Finally, an efficient technique for evaluating the directivity of an aperiodic array of half-wave dipoles is developed for use in conjunction with genetic algorithms.

Optimization of thinned aperiodic linear phased arrays using genetic algorithms to reduce grating lobes during scanning

As various enabling technologies advance, conformal phased arrays are finding more numerous applications. Because a conformal array is curved, new far field pattern behaviors emerge and many of the traditional linear and planar phased array synthesis methods are not valid. This paper starts by reviewing the equations for the far field of a curved phased array, and provides a generalized definition of aperture efficiency appropriate for conformal arrays. A modified Bernstein polynomial, defined with just five parameters, is introduced which provides a flexible method to specify a variety of smooth unimodal amplitude distributions that are shown to give good sidelobe levels and aperture efficiencies. By using particle swarm optimization of the modified Bernstein polynomial parameters constrained to provide a specified aperture efficiency, a family of aperture distributions and corresponding far field patterns is produced that allows aperture efficiency to be traded for sidelobe level.

Efficiency-constrained particle swarm optimization of a modified bernstein polynomial for conformal array excitation amplitude synthesis

Genetic algorithms are commonly used to solve many optimization and synthesis problems. An important issue facing the user is the selection of genetic algorithm parameters, such as mutation rate, mutation range, and number of crossovers. This paper demonstrates a real-valued genetic algorithm that simultaneously adapts several such parameters during the optimization process. This adaptive algorithm is shown to outperform its static counterparts when used to synthesize the phased array weights to satisfy specified far-field sidelobe constraints, and can perform amplitude-only, phase-only, and complex weight synthesis. When compared to conventional static parameter implementations, computation time is saved in two ways: 1) The algorithm converges faster and 2) the need to tune parameters by hand (generally done by repeatedly running the code with different parameter choices) is greatly reduced. By requiring less iteration to solve a given problem, this approach may benefit electromagnetic optimization problems with expensive cost functions, since genetic algorithms generally require many function evaluations to converge. The adaptive process also provides insight into the qualitative importance of parameters, and dynamically adjusting the mutation range is found to be especially beneficial.

A simultaneous parameter adaptation scheme for genetic algorithms with application to phased array synthesis

Porous silicon has received considerable attention for its potential as a silicon‐based visible light emitter In this letter we introduce the lateral photovoltaic effect in porous silicon and show its origin in amphoteric dangling bond traps at the porous silicon surface, which also play a role in the light‐induced photoluminescence degradation The use of the lateral photoeffect for a wide area position‐sensitive visible light detector is demonstrated The lateral photoeffect also provides a new electrical technique to study the atmospheric interactions of porous silicon, with possible applications for vapor detection

D.W. Boeringer

Papers

Particle swarm optimization versus genetic algorithms for phased array synthesis

Optimization of thinned aperiodic linear phased arrays using genetic algorithms to reduce grating lobes during scanning

Efficiency-constrained particle swarm optimization of a modified bernstein polynomial for conformal array excitation amplitude synthesis

A simultaneous parameter adaptation scheme for genetic algorithms with application to phased array synthesis

Lateral photovoltaic effect in porous silicon