Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

The artificial bee colony (ABC) algorithm is an important branch of evolutionary algorithms and has been shown to be competitive with other algorithms for solving optimisation problems. However, there are always some insufficiencies in ABC algorithm such as lower convergence speed and easily get trapped in the local optima. To increase depth search capabilities of onlooker bees and ensure scout bees do not discard the current optimal solution, we proposed a modified ABC algorithm (denoted as ORABC) based on the optimisation strategy and retained strategy of the best individual in this paper. Compared with ABC algorithm, ABC* (added retained strategy to ABC) and ORABC algorithm, experiments are conducted on a set of numerical benchmark functions. The numerical simulation results demonstrate that ORABC algorithm improves the convergence characteristics of ABC algorithm and provides very remarkable performance in solving complex numerical optimisation problems compared to original algorithm.

An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling

PurposeThe purpose of this paper is to use an ABC algorithm to improve the thrust–torque ratio of a rotating-wing unmanned aerial vehicle (UAV) model.Design/methodology/approachThe design of UAVs, such as aircraft, drones, helicopters, has become one of the popular engineering areas with the development of technology. This study aims to improve the value of thrust–torque ratio of an unmanned helicopter. For this purpose, an unmanned helicopter was built at the Faculty of Aeronautics and Astronautics, Erciyes University. The maximum thrust–torque ratio was calculated considering the blade length, blade chord width, blade mass density and blade twist angle. For calculation, artificial bee colony (ABC) algorithm was used. By using ABC algorithm, the maximum thrust–torque ratio was obtained against the optimum input values. For this purpose, a model with four inputs and a single output is formed. In the generated system model, optimum thrust–torque ratio was calculated by changing the input values used in the ±5% range. As a result of this study, approximately 31% improvement was achieved. According to these results, the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.FindingsAccording to these results, approximately 31% improvement was achieved, and the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.Research limitations/implicationsIt takes a long time to obtain the optimum thrust–torque ratio value through the ABC algorithm method.Practical implicationsUsing ABC algorithm provides to improve the value of thrust–torque ratio of an unmanned helicopter. With this algorithm, unmanned helicopter flies more than ever. Thus, the presented method based on the ABC algorithm is more efficient.Social implicationsThe application of the ABC algorithm method can be used effectively to calculate the thrust–torque ratio in UAV.Originality/valueProviding an original and penetrating a method that saves time and reduces the cost to improve the value of thrust–torque ratio of an unmanned helicopter.

Introduction:Reliability analysis is a good tool to deal with the uncertainty and has been widely used in the engineering system. The first order reliability method (FORM) is generally used to calculate the reliability index but FORM is time-consuming and requires derivative computing.Methods:Artificial Bee Colony (ABC) algorithm is a very simple, robust and population-based stochastic optimization algorithm. In this study, an ABC-based reliability analysis was proposed to calculate the reliability index of engineering system through combining Artificial Bee Colony (ABC) algorithm with FORM. FORM was adopted to calculate the reliability index and design point. ABC is used to solve the constrained optimization about FORM. The procedure of ABC-based reliability analysis was presented in detail.Results and Conclusion:The proposed method was verified by two classic examples and then applied to geotechnical engineering. The results show that the ABC algorithm can effectively solve the global optimization problem in FORM. Results demonstrate that ABC-based reliability analysis is a good approach to obtain the reliability index and design point with a good accuracy so that it can be applied to analyze the reliability of a complex engineering system.

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

Gbest-guided artificial bee colony algorithm for numerical function optimization

Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-divided structure, the ABC algorithm has been successfully applied to different types of optimization problems. However, some assumptions that are made with the purpose of reducing implementation difficulties about the sophisticated behaviours of employed, onlooker, and scout bees still require changes with the more literal procedures. In this study, the ABC algorithm and its well-known variants are powered by adding a new control mechanism in which the decision-making process of the employed bees managing transitions to the dance area is modeled. Experimental studies with different types of problems and analysis about the parallelization showed that the newly proposed approach significantly improved the qualities of the final solutions and convergence characteristics compared to the standard implementations of the ABC algorithms.

Based on the coupled vertical-torsional free vibration records of the spring-suspended section model of bridge decks, an improved Artificial Bee Colony (ABC) algorithm is proposed to identify the flutter derivatives. The ABC algorithm is a novel biological-inspired optimization algorithm, comparing with other iteration methods, the ABC algorithm can facilitate the identification process with no need for initial value. The standard ABC algorithm is good at exploration but poor at exploitation. To enhance the exploitation of the ABC algorithm, Powell’s method is used as a local search tool and a nonlinear factor is introduced for convergence control. Search equations applied to generate candidate solutions in the onlookers and scout bees phase are modified to improve the search ability of this method. The objective function in the optimization is weighted residual sum of squares between measured records and predicted values, where the weighting factors are optimized by an iteration process. The modified ABC algorithm is tested on several benchmark functions compared with the standard ABC algorithm. Simulation results show that the proposed algorithm has a higher convergence speed, better solution quality and stronger robustness. In order to investigate the effectiveness of the modified ABC algorithm in the flutter derivatives identification, numerical simulations of an ideal thin-plate model are carried out. The identification results show that the modified ABC algorithm for flutter derivatives identification of bridge decks is robust and reliable.

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

Archive-based multi-criteria Artificial Bee Colony algorithm for whole test suite generation

In the field of data analysis, clustering is a powerful technique which groups the data into different subsets using a distance function. Data belonging to the same subset are similar in nature and offer heterogeneity to the data that reside in other subsets. Clustering has proved its potentiality in various fields such as bioinformatics, pattern recognition, image processing and many more. In this paper, a two-step artificial bee colony (ABC) algorithm is proposed for efficient data clustering. In two-step ABC algorithm, the initial positions of food sources are identified using the K-means algorithm instead of random initialization. Along this, to discover the promising search areas, an improved solution search equation based on social behavior of PSO is applied in the onlooker bee phase of ABC algorithm and abandoned food source location is found by using Hooke and Jeeves-based direct search method. Five benchmark and two artificial datasets are applied to validate the proposed modifications in the ABC algorithm, and results of this study are compared with other well-known clustering algorithms. Both the experimental and statistical analyses show that improvements in ABC algorithm have an advantage over the conventional ABC algorithm for solving clustering problems.

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms.

A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.