On Parameter Identification for Reaction-Dominated Pore-Scale Reactive Transport Using Modified Bee Colony Algorithm

Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification; thus, there is a certain similarity between the parameter identification and optimization. A number of rigorous and efficient algorithms for optimization problems were developed in recent decades for the case of a convex functional. In the case of a non-convex functional, the metaheuristic algorithms dominate. This paper discusses an optimization method called modified bee colony algorithm (MBC), which is a modification of the standard bees algorithm (SBA). The SBA is inspired by a particular intelligent behavior of honeybee swarms. The algorithm is adapted for the parameter identification of reaction-dominated pore-scale transport when a non-convex functional has to be minimized. The algorithm is first checked by solving a few benchmark problems, namely finding the minima for Shekel, Rosenbrock, Himmelblau and Rastrigin functions. A statistical analysis was carried out to compare the performance of MBC with the SBA and the artificial bee colony (ABC) algorithm. Next, MBC is applied to identify the three parameters in the Langmuir isotherm, which is used to describe the considered reaction. Here, 2D periodic porous media were considered. The simulation results show that the MBC algorithm can be successfully used for identifying admissible sets for the reaction parameters in reaction-dominated transport characterized by low Pecklet and high Damkholer numbers. Finite element approximation in space and implicit time discretization are exploited to solve the direct problem.

Optimization of Model Predictive Control Weights for Control of Permanent Magnet Synchronous Motor by Using the Multi Objective Bees Algorithm

In this study, the model predictive control (MPC) method was used within the scope of the control of the permanent magnet synchronous motor (PMSM). The strongest aspect of the MPC, the ability to control multiple components with a single function, is also one of the most difficult parts of its design. The fact that each component of the function has different effects requires assigning different weight coefficients to these components. In this study, the Bees Algorithm (BA) is used to determine the weights. Using the multi-objective function in BA, it has been tried to determine the weights that reduce the current values together with the speed error. Three different PI controllers have been designed to compare the MPC method. The coefficients of one of these are tuned with BA. Good Gain Method and Tyreus-Luyben Method were used in the other two. As a result of experimental studies, it has been observed that MPC can control PMSM more smoothly and accurately than PI controllers, with weights optimized with BA. With MPC, PMSM has been controlled with 15% settling time than other controllers and also with no overshoot.

Optimal PID and fuzzy logic based position controller design of an overhead crane using the Bees Algorithm

Different industrial applications frequently use overhead cranes for moving and lifting huge loads. It applies to civil construction, metallurgical production, rivers, and seaports. The primary purpose of this paper is to control the motion/position of the overhead crane using a PID controller using Genetic Algorithms (GA) and Bee Algorithms (BA) as optimization tools. Moreover, Fuzzy Logic modified PID Controller is applied to obtain better controller parameters. The mathematical model uses an analytical method, and the PID model employs Simulink in MATLAB. The paper presents the PID parameters determination with a different approach. The development of membership functions, fuzzy rules employ the Fuzzy Logic toolbox. Both inputs and outputs use triangular membership functions. The result shows that the optimized value of the PID controller with the Ziegler-Nichols approach is time-consuming and will provide only the initial parameters. However, PID parameters obtained with the optimization method using GA and BA reached the target values. The results obtained with the fuzzy logic controller (0.227% overshoot) show improvement in overshoot than the conventional PID controller (0.271% overshoot).

Development of a Method to Measure the Quality of Working Life Using the Improved Metaheuristic Grasshopper Optimization Algorithm

This paper provides a method to numerically measure the quality of working life based on the reduction of human resource risks. It is conducted through the improved metaheuristic grasshopper optimization algorithm in two phases. First, a go-to study is carried out to identify the relationship between quality of working life and human resource risks in the capital market and to obtain the factors from quality of working life which reduce the risks. Then, a method is presented for the numerical measurement of these factors using a fuzzy inference system based on an adaptive neural network and a new hybrid method called the improved grasshopper optimization algorithm. This algorithm consists of the grasshopper optimization algorithm and the bees algorithm. It is found that the newly proposed method performs better and provides more accurate results than the conventional one.

The Bees Algorithm Tuned Sliding Mode Control for Load Frequency Control in Two-Area Power System

This paper proposes a design of Sliding Mode Control (SMC) for Load Frequency Control (LFC) in a two-area electrical power system. The mathematical model design of the SMC is derived based on the parameters of the investigated system. In order to achieve the optimal use of the proposed controller, an optimisation tool called the Bees Algorithm (BA) is suggested in this work to tune the parameters of the SMC. The dynamic performance of the power system with SMC employed for LFC is studied by applying a load disturbance of 0.2 pu in area one. To validate the supremacy of the proposed controller, the results are compared with those of recently published works based on Fuzzy Logic Control (FLC) tuned by Teaching–Learning-Based Optimisation (TLBO) algorithm and the traditional PID optimised by Lozi map-based Chaotic Optimisation Algorithm (LCOA). Furthermore, the robustness of SMC-based BA is examined against parametric uncertainties of the electrical power system by simultaneous changes in certain parameters of the testbed system with 40% of their nominal values. Simulation results prove the superiority and the robustness of the proposed SMC as an LFC system for the investigated power system.

Improving Bees Algorithm Using Gradual Search Space Reduction

The aim of this research is to propose a new technique to improve Bees Algorithm. Bees Algorithm is one of the well-known metaheuristic optimization method which have been subject to several attempts to improve it by overcoming some of the weaknesses. The suggested method is derived from the numerical optimization methods, namely bracketing and region elimination methods. It employs an adaption of the regional elimination method to achieve abandonment and reduction of search space within the Bees Algorithm. The utilization of the exhaustive search involves exploring the whole search space to find the optimum at equally located intervals. To assess performance, the proposed method was evaluated on twenty-four benchmark functions and two engineering problems. The acquired result indicated a statistically significant improvement.

Load Frequency Control Based on the Bees Algorithm for the Great Britain Power System

This paper focuses on using the Bees Algorithm (BA) to tune the parameters of the proposed Fuzzy Proportional–Integral–Derivative with Filtered derivative (Fuzzy PIDF), Fractional Order PID (FOPID) controller and classical PID controller developed to stabilize and balance the frequency in the Great Britain (GB) power system at rated value. These controllers are proposed to meet the requirements of the GB Security and Quality of Supply Standard (GB-SQSS), which requires frequency to be brought back to its nominal value after a disturbance within a specified time. This work is extended to employ the proposed fuzzy structure controller in a dual-area interconnected power system. In comparison with controllers tuned by Particle Swarm Optimization (PSO) and Teaching Learning-Based Optimization (TLBO) used for the same systems, simulation results show that the Fuzzy PIDF tuned by BA is able to significantly reduce the deviation in the frequency and tie-line power when a sudden disturbance is applied. Furthermore, the applied controllers tuned by BA including the Fuzzy PIDF prove their high robustness against a wide range of system parametric uncertainties and different load disturbances.