Employing Quantum Fruit Fly Optimization Algorithm for Solving Three-Dimensional Chaotic Equations

Abstract

In a chaotic system, deterministic, nonlinear, irregular, and initial-condition-sensitive features are desired. Due to its chaotic nature, it is difficult to quantify a chaotic system’s parameters. Parameter estimation is a major issue because it depends on the stability analysis of a chaotic system, and communication systems that are based on chaos make it difficult to give accurate estimates or a fast rate of convergence. Several nature-inspired metaheuristic algorithms have been used to estimate chaotic system parameters; however, many are unable to balance exploration and exploitation. The fruit fly optimization algorithm (FOA) is not only efficient in solving difficult optimization problems, but also simpler and easier to construct than other currently available population-based algorithms. In this study, the quantum fruit fly optimization algorithm (QFOA) was suggested to find the optimum values for chaotic parameters that would help algorithms converge faster and avoid the local optimum. The recommended technique used quantum theory probability and uncertainty to overcome the classic FA’s premature convergence and local optimum trapping. QFOA modifies the basic Newtonian-based search technique of FA by including a quantum behavior-based searching mechanism used to pinpoint the position of the fruit fly swarm. The suggested model has been assessed using a well-known Lorenz system with a specified set of parameter values and benchmarked signals. The results showed a considerable improvement in the accuracy of parameter estimates and better estimation power than state-of-the art parameter estimation approaches.

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

“…As can be seen from Table 4 , the EO-GRNN performs the best, while the POS-GRNN and the FOA-GRNN have essentially the same accuracy, which can also be reflected in the absorption coefficient plot. This situation may be because PSO and FOA choose a fixed step size for exploration during optimization [ 41 ]. At the same time, EO updates the current search parameters at each iteration during optimization and compares the target values among the five candidate concentrations to avoid falling into a local optimum solution.…”

A Hybrid Chaotic-Based Multiobjective Differential Evolution Technique for Economic Emission Dispatch Problem

“…The Lorenz attractor was introduced by Edward Lorenz [67] for modeling the phenomenon of thermal convection in fluids. Mathematically, the Lorenz attractor is described by a three-dimensional system of ordinary differential equations, as follows [68]. In Equation (15), N LM is the number of chaotic numbers generated by the logistic map.…”

“…γ, ρ, and δ are the Lorenz attractor parameters. Typical values of these constants are as follows [68]. γ = 10, ρ = 28 and δ = 8/3.…”

Carbon Trading Price Prediction of Three Carbon Trading Markets in China Based on a Hybrid Model Combining CEEMDAN, SE, ISSA, and MKELM

“…To further verify the validity of the established CEEMDAN-SE-ISSA-MKELM model in CTP forecasting, the single model, including Markov forecasting model (MFM) [37], ARIMA [38], ELM [39], LSSVM [40], LSTM [38], poly kernel ELM (pELM), RBF kernel ELM (rELM), and MKELM (combining poly kernel and RBF kernel), as well as the combined model, including CEEMDAN-MKELM, EMD-MKELM [41], CEEMD-MKELM [42], CEEMDAN-SE-MKELM, EMD-SE-MKELM, CEEMD-SE-MKELM, CEEMDAN-SE-FOA-MKELM [43], CEEMDAN-SE-PSO-MKELM [44], and CEEMDAN-SE-SSA-MKELM, are selected as the comparison model. The values of MAPE, RMSE, and R2 for the forecasted results in Guangdong's carbon market are illustrated in Figure 6.…”