Application of the variation operator in a genetic algorithm for the synthesis of fuzzy controllers

Objective. This article studies the problem of increasing the efficiency of fuzzy controller synthesis in a control system using a genetic algorithm. The best parameters of the fuzzy controller are selected using the crossing-over and mutation operators in the genetic algorithm. The operation of the mutation operator can lead to the formation of an incorrect set of parameters, which complicates the procedure for synthesizing a fuzzy controller.Methods. Arrays of parameter sets of membership functions, conclusions, and rule weights that are included in the fuzzy controller are compiled using mathematical simulation. The mechanism of operation of single-point and two-point variation operators in the genetic algorithm is described by the simulation modeling.Results. Mathematical models of single-point and two-point variation operators for the genetic algorithm are proposed. The mechanism for changing the values of elements in the array of a set of parameters of a fuzzy controller with one input and output variable is presented.Conclusion. Replacing the mutation operator with the variation operator eliminates the formation of incorrect sets of parameters of the fuzzy controller in the control system.

Oscillation and variation for the Riesz transform associated with Bessel operators

Let λ > 0 and letbe the Bessel operator on ℝ+ := (0,∞). We show that the oscillation operator 𝒪(RΔλ,∗) and variation operator 𝒱ρ(RΔλ,∗) of the Riesz transform RΔλ associated with Δλ are both bounded on Lp(ℝ+, dmλ) for p ∈ (1,∞), from L1(ℝ+, dmλ) to L1,∞(ℝ+, dmλ), and from L∞(ℝ+, dmλ) to BMO(ℝ+, dmλ), where ρ ∈ (2,∞) and dmλ(x) := x2λ dx. As an application, we give the corresponding Lp-estimates for β-jump operators and the number of up-crossings.

Influence of Probability of Variation Operator on the Performance of Quantum-Inspired Evolutionary Algorithm for 0/1 Knapsack Problem

Quantum-Inspired Evolutionary Algorithm (QEA) has been shown to be better performing than classical Genetic Algorithm based evolutionary techniques for combinatorial optimization problems like 0/1 knapsack problem. QEA uses quantum computing-inspired representation of solution called Q-bit individual consisting of Q-bits. The probability amplitudes of the Q-bits are changed by application of Q-gate operator, which is classical analogous of quantum rotation operator. The Q-gate operator is the only variation operator used in QEA, which along with some problem specific heuristic provides exploitation of the properties of the best solutions. In this paper, we analyzed the characteristics of the QEA for 0/1 knapsack problem and showed that a probability in the range 0.3 to 0.4 for the application of the Q-gate variation operator has the greatest likelihood of making a good balance between exploration and exploitation. Experimental results agree with the analytical finding.