This paper 1 focuses on using the Bees Algorithm in both its basic and enhanced forms to tune the parameters of a fuzzy logic controller developed to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. A linear quadratic regulator (LQR) was first developed to obtain the scaling gains needed to design the fuzzy logic controller. Simulation results confirmed that using the Bees Algorithm to optimise the membership functions and the scaling gains of the fuzzy system improved the controller performance.
Abstract. This paper presents using the Bees Algorithm for Environmental/Economic power Dispatch problem which is formulated as a nonlinear constrained multi-objective optimisation problem. In this problem, both fuel cost and emission are to be simultaneously minimised. Simulation results presented for the standard IEEE 30-bus system using the Bees Algorithm with Weighted Sum are compared to other previous approaches and the comparison shows the superiority of the proposed Bees Algorithm and confirms its potential to solve the multiobjective EED problem.
This paper presents the result of research in developing a novel training model for Adaptive NeuroFuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial Neural Networks with the Takagi-Sugeno Fuzzy Inference System to approximate nonlinear functions. Therefore, it is considered as a Universal Estimator. The original algorithm used in ANFIS training process has a hybrid model that uses Steepest Decent Derivative; therefore, it inherits low convergence rate and local minima during training. In this study, a training algorithm is proposed that combines Bees Algorithm (BA) and Least Square Estimation (LSE) (BA-LSE). The local and global exploration of BA as integrates with the best-fit solution of the LSE improves current shortcomings of ANFIS training process. The proposed training algorithm is examined under three different scenarios of function approximation, time series prediction, and classification experiments in order to verify the promising improvements in the training process of ANFIS. The experimental results validate high generalization capabilities of the BA-LSE training algorithm in comparison to the original hybrid training model of ANFIS. The new training model also enhances local minima avoidance and has high convergence rate.
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