Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Linear robust control
Modern control engineering (3rd ed.)
Modern control engineering (3rd ed.)
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Evolutionary Computation for Modeling and Optimization
Evolutionary Computation for Modeling and Optimization
An evolutionary-based approach for solving a capacitated hub location problem
Applied Soft Computing
DEPSO and PSO-QI in digital filter design
Expert Systems with Applications: An International Journal
Evolutionary neural networks and DNA computing algorithms for dual-axis motion control
Engineering Applications of Artificial Intelligence
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
Stock index tracking by Pareto efficient genetic algorithm
Applied Soft Computing
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This paper develops an innovative optimization method, real structured genetic algorithm (RSGA), which combines the advantages of traditional real genetic algorithm (RGA) with structured genetic algorithm (SGA), and applies it for digital filter and control design optimization problems. For infinite impulse response (IIR) filter designs, the proposed approach fulfills all types of filters by minimizing the order of the filter and the absolute error of both passband and stopband. Both system structure and parametric variables are simultaneously optimized via the proposed chromosome scheme. The approach has also been extended to deal with robust control design problems. The approach offers an effective method for designing an optimal controller with robust stability. Simulation and experimental results conveys the excellence of the proposed algorithm over traditional approaches in convergence speed, performance, cost effectiveness, and attains simpler structure.