Global gbest guided-artificial bee colony algorithm for numerical function optimization

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing

STATISTICALLY GUIDED ARTIFICIAL BEE COLONY ALGORITHM

Artificial Bee Colony algorithm is one of the naturally inspired meta heuristic method. As usual, in a meta heuristic method, intuitively appealing way to have better results is extending calculation time or increasing the fitness evaluation count. But the desired way is acquiring better results with less computation. So in this work a modified Artificial Bee Colony algorithm which can find better results with same computation is developed by benefiting statistical observations

A guided artificial bee colony (GABC) heuristic for permutation flowshop scheduling problem (PFSP)

Flowshop is the most common production system in the industry, and there are many documented efforts to improve the performance of the flowshop. The range spreads from the usage of heuristics to metaheuristics, and one of the promising methods is NEH (Nawaz, Enscore & Ham) heuristics. This study aims to improve NEH, using an enhanced version of Artificial Bee Colony (ABC) algorithm because the original one has the problem of slow converge speed. As a result, this study will propose a mechanism to improve the convergence speed of ABC because faster convergence speed is the ability to find high-quality results in lesser iterations compared to others. The study clusters the Employed Bees (EB) and Onlooker Bees (OB) into several groups: Total Greedy, Semi Greedy and Non-Greedy. Upon completion, the study selected the Total Greedy (3+0+0) because of the leading performance in makespan value (performance indicator), and the author used it for the rest of this study. This study proposed two variants of the guided initial ABC or Guided Artificial Bee Colony (GABC) with one variant (NEH-based ABC), employing the concept of NEH and the second variant (GABC), employing the concept of NEH and First Job Sequence Arrangement Method. The study experimented according to ten datasets of Taillard benchmark and divided the experiments into several categories and the experiments run every data for several iterations, and for each dataset, there are 20 replications. This study compared the performance of NEH, ABC, NEH-based ABC and GABC, which also act as the validation process. Based on the results, ABC produced inconsistent results for a significant amount of times and interestingly, GABC, NEHbased ABC and ABC produced 68.75%, 63.33% and 0.01% results that are better than NEH, respectively. The data also shows that GABC is 37.9% better than its variant. Finally, the author can conclude that this study demonstrated the slow convergence issue of ABC

A quick gbest guided artificial bee colony algorithm for stock market prices prediction

The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values

An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

Learning an Artificial Neural Network (ANN) is an optimization task since it is desirable to find optimal weight sets of an ANN in the training process. Different equations are used to guide the network for providing an accurate result with less training and testing error. Most of the training algorithms focus on weight values, activation functions, and network structures for providing optimal outputs. Backpropagation (BP) learning algorithm is the well-known learning technique that trained ANN. However, some difficulties arise where the BP cannot get achievements without trapping in local minima and converge very slow in the solution space. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony (ABC) algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron (MLP). Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. They are: Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial Bee Colony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. These improved algorithms were used to increase the exploration, exploitation and keep them balance for getting optimal results for a given task. Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. The simulation results of the MLP trained with improved algorithms were compared with that when trained with the standard BP, ABC, Global ABC and Particle Swarm Optimization algorithm. From the experimental analysis, the proposed improved algorithms get better the classification efficacy for time series prediction and Boolean function classification. Moreover, these improved algorithm's success to get high accuracy and optimize the best network's weight values for training the MLP

Power Quality Improvement of a Distribution Network for Sustainable Power Supply

This paper presents a heuristic technique for improving power quality of a distribution network for sustainable electric power supply using shunt capacitor placement. The issue of power loss has been a major threat to a distribution network. A distribution network is expected to operate at certain voltage level to meet consumer’s energy demand. Power flow studies has been conducted using the Newton Raphson’s technique at the 30 bus, 11 kV Onuiyi-Nsukka distribution network. It was found that the voltage profile at buses 19 and 26 were critically violated with voltage amplitudes of 0.72 pu and 0.79 pu respectively. The feeder power quality was improved using a heuristic technique and the installation of a 1200KVAr shunt capacitor to keep bus voltage amplitudes within the legal limit of (0.95-1.05) pu. The voltage profile, active and reactive power losses on the network were determined. Active power loss and reactive power loss was reduced from 0.27MW to 0.12MW and 0.76Mvar to 0.14Mvar, respectively. Therefore, the voltage profile is enhanced and the power loss significantly reduced

New approach on global optimization problems based on meta-heuristic algorithm and quasi-Newton method

This paper presents an innovative approach in finding an optimal solution of multimodal and multivariable function for global optimization problems that involve complex and inefficient second derivatives. Artificial bees colony (ABC) algorithm possessed good exploration search, but the major weakness at its exploitation stage. The proposed algorithms improved the weakness of ABC algorithm by hybridized with the most effective gradient based method which are Davidon-Flecher-Powell (DFP) and Broyden-Flecher-Goldfarb-Shanno (BFGS) algorithms. Its distinguished features include maximizing the employment of possible information related to the objective function obtained at previous iterations. The proposed algorithms have been tested on a large set of benchmark global optimization problems and it has shown a satisfactory computational behaviour and it has succeeded in enhancing the algorithm to obtain the solution for global optimization problems

Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)