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In this paper, designing of the LQR controller and observer with intelligent tools for the triple inverted pendulum is investigated. Intelligent tools are considered as GA and GA-PSO optimization algorithms and fuzzy logic to qualify achieving LQR gains. The pendulum is swung up from the vertical position to the unstable position. The rules for the controlled swing up are heuristically achieved such...
The problem of determining simultaneously the model order and coefficient of an Autoregressive Moving Average (ARMA) model is examined in this paper. An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. The first level searches for the appropriate model order while the second level computes the optimal/sub-optimal corresponding parameters...
Optimization problems with intervals and hybrid indices are common in real-world applications. Previous theories and methods suitable for them, however, are few. We present a large population evolutionary algorithm with a user's interval preferences to effectively solve the problems above in this study. In this algorithm, a large population is adopted to improve the performance of the algorithm in...
In engineering design optimization, derivatives are computationally expensive and/or unreliable, therefore evolutionary optimization techniques are preferred, such as Particle Swarm Optimization. Particle Swarm Optimization is still young in development and is being expanded to many different areas such as equality constrained optimization problems and multi-objective optimization. This paper proposes...
This paper presents a modified particle swarm optimization (PSO) algorithm to improve the performance of standard PSO. The proposed approach is called HPSO, which modifies the original velocity updating equation of PSO. In order to verify the performance of HPSO, we test it on ten well-known benchmark optimization functions. The simulation results show that HPSO obtains better performance than standard...
In this paper, the authors propose a new evolutionary optimization i.e. synchronous bacterial foraging optimization (SBFO). The SBFO can be used for optimization of multimodal and high dimensional functions. It also enhances computational throughput and global search capability. The convergence of original BFO to the optimum value is very slow and its performance is also heavily affected with increased...
The oil industries in the entire World and particularly in Mexico, have been taking an important relevance. There are two major challenges in this industry. The first one is the exploration and utilization of crude oil in deep sea, the second one is the scarce of light crude, the actual production report an increment of heavy crude, generating corrosion steel in the extraction and refinement processes...
The particle swarm optimization algorithm with constriction factor (CFPSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. A chaotic optimization-based simple particle swarm optimization equation with constriction factor is developed. Piecewise linear chaotic map is employed to perform chaotic optimization...
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