Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented

Novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation

© 2014 Elsevier B.V. All rights reserved. This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence resulting in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria movement affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while de.ning a small step-size gives high accuracy but produces slower convergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as parametric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator

Electrical Load Prediction using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning approach

Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic systems whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO

Cúmulo de partículas coevolutivo cooperativo usando lógica borrosa para la optimización a gran escala

A cooperative coevolutionary framework can improve the performance of optimization algorithms on large-scale problems. In this paper, we propose a new Cooperative Coevolutionary algorithm to improve our preliminary work, FuzzyPSO2. This new proposal, called CCFPSO, uses the random grouping technique that changes the size of the subcomponents in each generation. Unlike FuzzyPSO2, CCFPSO’s re-initialization of the variables, suggested by the fuzzy system, were performed on the particles with the worst fitness values. In addition, instead of updating the particles based on the global best particle, CCFPSO was updated considering the personal best particle and the neighborhood best particle. This proposal was tested on large-scale problems that resemble real-world problems (CEC2008, CEC2010), where the performance of CCFPSO was favorable in comparison with other state-of-the-art PSO versions, namely CCPSO2, SLPSO, and CSO. The experimental results indicate that using a Cooperative Coevolutionary PSO approach with a fuzzy logic system can improve results on high dimensionality problems (100 to 1000 variables).Un marco coevolutivo cooperativo puede mejorar el rendimiento de los algoritmos de optimización en problemas a gran escala. En este trabajo, proponemos un nuevo algoritmo coevolutivo cooperativo para mejorar nuestro trabajo preliminar, FuzzyPSO2. Esta nueva propuesta, denominada CCFPSO, utiliza la técnica de agrupación aleatoria que cambia el tamaño de los subcomponentes en cada generación. A diferencia de FuzzyPSO2, la reinicialización de las variables de CCFPSO, sugerida por el sistema difuso, se realizaron sobre las partículas con los peores valores de fitness. Además, en lugar de actualizar las partículas basándose en la mejor partícula global, CCFPSO se actualizó considerando la mejor partícula personal y la mejor partícula del vecindario. Esta propuesta se probó en problemas a gran escala que se asemejan a los del mundo real (CEC2008, CEC2010), donde el rendimiento de CCFPSO fue favorable en comparación con otras versiones de PSO del estado del arte, a saber, CCPSO2, SLPSO y CSO. Los resultados experimentales indican que el uso de un enfoque PSO coevolutivo cooperativo con un sistema de lógica difusa puede mejorar los resultados en problemas de alta dimensionalidad (de 100 a 1000 variables).Facultad de Informátic

A modified particle swarm optimization algorithm for the optimization of a fuzzy classification subsystem in a series hybrid electric vehicle

Algoritmi optimizacije temeljeni na optimizaciji roja čestica (PSO - particle swarm optimization) su jednostavne i lako primjenljive metode male računske složenosti što ih čini podesnim alatom za rješavanje opsežnih nelinearnih problema optimizacije. U radu je prikazana modificirana verzija originalne metode kombiniranjem PSO i lokalne metode pretraživanja na kraju svakog ciklusa ponavljanja. Novi se algoritam primjenjuje u zadatku optimizacije parametara podsustava fuzzy klasifikacije u serijskom hibridnom električnom vozilu u cilju smanjenja ispuštanja štetnih zagađivača. Novom se metodom uz primjenu sličnih parametara osigurava vrijednost veće prikladnosti nego bilo s originalnim PSO algoritmom ili algoritmom umjetnog imunološkog sustava zasnovanog na klonskoj selekciji (CLONALG).Particle swarm optimization (PSO) based optimization algorithms are simple and easily implementable techniques with low computational complexity, which makes them good tools for solving large-scale nonlinear optimization problems. This paper presents a modified version of the original method by combining PSO with a local search technique at the end of each iteration cycle. The new algorithm is applied for the task of parameter optimization of a fuzzy classification subsystem in a series hybrid electric vehicle (SHEV) aiming at the reduction of the harmful pollutant emission. The new method ensured a better fitness value than either the original PSO algorithm or the clonal selection based artificial immune system algorithm (CLONALG) by using similar parameters

Fuzzy adaptive emperor penguin optimizer for global optimization problems

The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. Mixed results have been observed in the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely f and l) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of parameters f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate this parameter tuning problem, an adaptive mechanism can be introduced in EPO. This research work proposes a fuzzy adaptive variant of EPO, namely FAEPO, to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters f and l throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve benchmark test functions and three global optimization problems: Team Formation Optimization (TFO), Low Autocorrelation Binary Sequence (LABS), and Modified Condition/ Decision coverage (MC/DC) test case generation problem were solved using the proposed algorithm. The respective solution results of the competing metaheuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance especially of its predecessor (EPO), an improved variant of EPO (i.e., IEPO), and a fuzzy-based variant of ChOA (i.e., FChOA) and gives superior performance against the competing metaheuristic algorithms. Moreover, the proposed FAEPO requires 50% less fitness function evaluation in each iteration than the ancestor EPO and exhibits competitive performance in terms of convergence and computational time against its predecessor (EPO) and other competing meta-heuristic algorithms with a 90% confidence level